{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ResnetTrick_s192bs32\n",
    "\n",
    "> size 192 bs 32"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# setup and imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install git+https://github.com/ayasyrev/model_constructor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install git+https://github.com/kornia/kornia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from kornia.contrib import MaxBlurPool2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.basic_train import *\n",
    "from fastai.vision import *\n",
    "from fastai.script import *\n",
    "from model_constructor.net import *\n",
    "from model_constructor.layers import SimpleSelfAttention, ConvLayer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ranger deep learning optimizer - RAdam + Lookahead combined.\n",
    "  #https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer\n",
    "\n",
    "  #Ranger has now been used to capture 12 records on the FastAI leaderboard.\n",
    "\n",
    "  #This version = 9.3.19  \n",
    "\n",
    "  #Credits:\n",
    "  #RAdam -->  https://github.com/LiyuanLucasLiu/RAdam\n",
    "  #Lookahead --> rewritten by lessw2020, but big thanks to Github @LonePatient and @RWightman for ideas from their code.\n",
    "  #Lookahead paper --> MZhang,G Hinton  https://arxiv.org/abs/1907.08610\n",
    "\n",
    "  #summary of changes: \n",
    "  #full code integration with all updates at param level instead of group, moves slow weights into state dict (from generic weights), \n",
    "  #supports group learning rates (thanks @SHolderbach), fixes sporadic load from saved model issues.\n",
    "  #changes 8/31/19 - fix references to *self*.N_sma_threshold; \n",
    "                  #changed eps to 1e-5 as better default than 1e-8.\n",
    "\n",
    "import math\n",
    "import torch\n",
    "from torch.optim.optimizer import Optimizer, required\n",
    "import itertools as it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Mish(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        print(\"Mish activation loaded...\")\n",
    "\n",
    "    def forward(self, x):  \n",
    "        #save 1 second per epoch with no x= x*() and then return x...just inline it.\n",
    "        return x *( torch.tanh(F.softplus(x))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class Ranger(Optimizer):\n",
    "\n",
    "    def __init__(self, params, lr=1e-3, alpha=0.5, k=6, N_sma_threshhold=5, betas=(.95,0.999), eps=1e-5, weight_decay=0):\n",
    "        #parameter checks\n",
    "        if not 0.0 <= alpha <= 1.0:\n",
    "            raise ValueError(f'Invalid slow update rate: {alpha}')\n",
    "        if not 1 <= k:\n",
    "            raise ValueError(f'Invalid lookahead steps: {k}')\n",
    "        if not lr > 0:\n",
    "            raise ValueError(f'Invalid Learning Rate: {lr}')\n",
    "        if not eps > 0:\n",
    "            raise ValueError(f'Invalid eps: {eps}')\n",
    "\n",
    "        #parameter comments:\n",
    "        # beta1 (momentum) of .95 seems to work better than .90...\n",
    "        #N_sma_threshold of 5 seems better in testing than 4.\n",
    "        #In both cases, worth testing on your dataset (.90 vs .95, 4 vs 5) to make sure which works best for you.\n",
    "\n",
    "        #prep defaults and init torch.optim base\n",
    "        defaults = dict(lr=lr, alpha=alpha, k=k, step_counter=0, betas=betas, N_sma_threshhold=N_sma_threshhold, eps=eps, weight_decay=weight_decay)\n",
    "        super().__init__(params,defaults)\n",
    "\n",
    "        #adjustable threshold\n",
    "        self.N_sma_threshhold = N_sma_threshhold\n",
    "\n",
    "        #now we can get to work...\n",
    "        #removed as we now use step from RAdam...no need for duplicate step counting\n",
    "        #for group in self.param_groups:\n",
    "        #    group[\"step_counter\"] = 0\n",
    "            #print(\"group step counter init\")\n",
    "\n",
    "        #look ahead params\n",
    "        self.alpha = alpha\n",
    "        self.k = k \n",
    "\n",
    "        #radam buffer for state\n",
    "        self.radam_buffer = [[None,None,None] for ind in range(10)]\n",
    "\n",
    "        #self.first_run_check=0\n",
    "\n",
    "        #lookahead weights\n",
    "        #9/2/19 - lookahead param tensors have been moved to state storage.  \n",
    "        #This should resolve issues with load/save where weights were left in GPU memory from first load, slowing down future runs.\n",
    "\n",
    "        #self.slow_weights = [[p.clone().detach() for p in group['params']]\n",
    "        #                     for group in self.param_groups]\n",
    "\n",
    "        #don't use grad for lookahead weights\n",
    "        #for w in it.chain(*self.slow_weights):\n",
    "        #    w.requires_grad = False\n",
    "\n",
    "    def __setstate__(self, state):\n",
    "        print(\"set state called\")\n",
    "        super(Ranger, self).__setstate__(state)\n",
    "\n",
    "\n",
    "    def step(self, closure=None):\n",
    "        loss = None\n",
    "        #note - below is commented out b/c I have other work that passes back the loss as a float, and thus not a callable closure.  \n",
    "        #Uncomment if you need to use the actual closure...\n",
    "\n",
    "        #if closure is not None:\n",
    "            #loss = closure()\n",
    "\n",
    "        #Evaluate averages and grad, update param tensors\n",
    "        for group in self.param_groups:\n",
    "\n",
    "            for p in group['params']:\n",
    "                if p.grad is None:\n",
    "                    continue\n",
    "                grad = p.grad.data.float()\n",
    "                if grad.is_sparse:\n",
    "                    raise RuntimeError('Ranger optimizer does not support sparse gradients')\n",
    "\n",
    "                p_data_fp32 = p.data.float()\n",
    "\n",
    "                state = self.state[p]  #get state dict for this param\n",
    "\n",
    "                if len(state) == 0:   #if first time to run...init dictionary with our desired entries\n",
    "                    #if self.first_run_check==0:\n",
    "                        #self.first_run_check=1\n",
    "                        #print(\"Initializing slow buffer...should not see this at load from saved model!\")\n",
    "                    state['step'] = 0\n",
    "                    state['exp_avg'] = torch.zeros_like(p_data_fp32)\n",
    "                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)\n",
    "\n",
    "                    #look ahead weight storage now in state dict \n",
    "                    state['slow_buffer'] = torch.empty_like(p.data)\n",
    "                    state['slow_buffer'].copy_(p.data)\n",
    "\n",
    "                else:\n",
    "                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)\n",
    "                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)\n",
    "\n",
    "                #begin computations \n",
    "                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']\n",
    "                beta1, beta2 = group['betas']\n",
    "\n",
    "                #compute variance mov avg\n",
    "                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)\n",
    "                #compute mean moving avg\n",
    "                exp_avg.mul_(beta1).add_(1 - beta1, grad)\n",
    "\n",
    "                state['step'] += 1\n",
    "\n",
    "\n",
    "                buffered = self.radam_buffer[int(state['step'] % 10)]\n",
    "                if state['step'] == buffered[0]:\n",
    "                    N_sma, step_size = buffered[1], buffered[2]\n",
    "                else:\n",
    "                    buffered[0] = state['step']\n",
    "                    beta2_t = beta2 ** state['step']\n",
    "                    N_sma_max = 2 / (1 - beta2) - 1\n",
    "                    N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)\n",
    "                    buffered[1] = N_sma\n",
    "                    if N_sma > self.N_sma_threshhold:\n",
    "                        step_size = math.sqrt((1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (N_sma_max - 2)) / (1 - beta1 ** state['step'])\n",
    "                    else:\n",
    "                        step_size = 1.0 / (1 - beta1 ** state['step'])\n",
    "                    buffered[2] = step_size\n",
    "\n",
    "                if group['weight_decay'] != 0:\n",
    "                    p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)\n",
    "\n",
    "                if N_sma > self.N_sma_threshhold:\n",
    "                    denom = exp_avg_sq.sqrt().add_(group['eps'])\n",
    "                    p_data_fp32.addcdiv_(-step_size * group['lr'], exp_avg, denom)\n",
    "                else:\n",
    "                    p_data_fp32.add_(-step_size * group['lr'], exp_avg)\n",
    "\n",
    "                p.data.copy_(p_data_fp32)\n",
    "\n",
    "                #integrated look ahead...\n",
    "                #we do it at the param level instead of group level\n",
    "                if state['step'] % group['k'] == 0:\n",
    "                    slow_p = state['slow_buffer'] #get access to slow param tensor\n",
    "                    slow_p.add_(self.alpha, p.data - slow_p)  #(fast weights - slow weights) * alpha\n",
    "                    p.data.copy_(slow_p)  #copy interpolated weights to RAdam param tensor\n",
    "\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(size=128, woof=1, bs=64, workers=None, **kwargs):\n",
    "    if woof:\n",
    "        path = URLs.IMAGEWOOF    # if woof \n",
    "    else:\n",
    "        path = URLs.IMAGENETTE\n",
    "    path = untar_data(path)\n",
    "    print('data path  ', path)\n",
    "    n_gpus = num_distrib() or 1\n",
    "    if workers is None: workers = min(8, num_cpus()//n_gpus)\n",
    "    return (ImageList.from_folder(path).split_by_folder(valid='val')\n",
    "            .label_from_folder().transform(([flip_lr(p=0.5)], []), size=size)\n",
    "            .databunch(bs=bs, num_workers=workers)\n",
    "            .presize(size, scale=(0.35,1))\n",
    "            .normalize(imagenet_stats))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_learn(\n",
    "        gpu:Param(\"GPU to run on\", str)=None,\n",
    "        woof: Param(\"Use imagewoof (otherwise imagenette)\", int)=1,\n",
    "        size: Param(\"Size (px: 128,192,224)\", int)=128,\n",
    "        alpha: Param(\"Alpha\", float)=0.99, \n",
    "        mom: Param(\"Momentum\", float)=0.95, #? 0.9\n",
    "        eps: Param(\"epsilon\", float)=1e-6,\n",
    "        bs: Param(\"Batch size\", int)=64,\n",
    "        mixup: Param(\"Mixup\", float)=0.,\n",
    "        opt: Param(\"Optimizer (adam,rms,sgd)\", str)='ranger',\n",
    "        sa: Param(\"Self-attention\", int)=0,\n",
    "        sym: Param(\"Symmetry for self-attention\", int)=0,\n",
    "        model: Param('model as partial', callable) = xresnet50\n",
    "        ):\n",
    " \n",
    "    if   opt=='adam' : opt_func = partial(optim.Adam, betas=(mom,alpha), eps=eps)\n",
    "    elif opt=='ranger'  : opt_func = partial(Ranger,  betas=(mom,alpha), eps=eps)\n",
    "    data = get_data(size, woof, bs)\n",
    "    learn = (Learner(data, model(), wd=1e-2, opt_func=opt_func,\n",
    "             metrics=[accuracy,top_k_accuracy],\n",
    "             bn_wd=False, true_wd=True,\n",
    "             loss_func = LabelSmoothingCrossEntropy(),))\n",
    "    print('Learn path', learn.path)\n",
    "    if mixup: learn = learn.mixup(alpha=mixup)\n",
    "    return learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(learn):\n",
    "    learn.recorder.plot_losses()\n",
    "    learn.recorder.plot_metrics()\n",
    "    learn.recorder.plot_lr(show_moms=True)\n",
    "Learner.plot = plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = 0.004\n",
    "epochs = 5\n",
    "moms = (0.95,0.95)\n",
    "start_pct = 0.72\n",
    "size=192\n",
    "bs=32"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ResBlock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NewResBlock(Module):\n",
    "    def __init__(self, expansion, ni, nh, stride=1, \n",
    "                 conv_layer=ConvLayer, act_fn=act_fn, bn_1st=True,\n",
    "                 pool=nn.AvgPool2d(2, ceil_mode=True), sa=False,sym=False, zero_bn=True):\n",
    "        nf,ni = nh*expansion,ni*expansion\n",
    "        self.reduce = noop if stride==1 else pool\n",
    "        layers  = [(f\"conv_0\", conv_layer(ni, nh, 3, stride=stride, act_fn=act_fn, bn_1st=bn_1st)),\n",
    "                   (f\"conv_1\", conv_layer(nh, nf, 3, zero_bn=zero_bn, act=False, bn_1st=bn_1st))\n",
    "        ] if expansion == 1 else [\n",
    "                   (f\"conv_0\",conv_layer(ni, nh, 1, act_fn=act_fn, bn_1st=bn_1st)),\n",
    "                   (f\"conv_1\",conv_layer(nh, nh, 3, stride=1, act_fn=act_fn, bn_1st=bn_1st)), #!!!\n",
    "                   (f\"conv_2\",conv_layer(nh, nf, 1, zero_bn=zero_bn, act=False, bn_1st=bn_1st))\n",
    "        ]\n",
    "        if sa: layers.append(('sa', SimpleSelfAttention(nf,ks=1,sym=sym)))\n",
    "        self.convs = nn.Sequential(OrderedDict(layers))\n",
    "        self.idconv = noop if ni==nf else conv_layer(ni, nf, 1, act=False)\n",
    "        self.merge =act_fn\n",
    "\n",
    "    def forward(self, x): \n",
    "        o = self.reduce(x)\n",
    "        return self.merge(self.convs(o) + self.idconv(o))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Constructor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = xresnet50(c_out=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.block = NewResBlock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pool = MaxBlurPool2d(3, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.pool = pool\n",
    "model.stem_pool = pool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mish activation loaded...\n"
     ]
    }
   ],
   "source": [
    "model.stem_sizes = [3,32,64,64]\n",
    "model.act_fn= Mish()\n",
    "model.sa = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## repr model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  model xresnet50\n",
       "  (stem): Sequential(\n",
       "    (conv_0): ConvLayer(\n",
       "      (conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (act_fn): Mish()\n",
       "    )\n",
       "    (conv_1): ConvLayer(\n",
       "      (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (act_fn): Mish()\n",
       "    )\n",
       "    (conv_2): ConvLayer(\n",
       "      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (act_fn): Mish()\n",
       "    )\n",
       "    (stem_pool): MaxBlurPool2d()\n",
       "  )\n",
       "  (body): Sequential(\n",
       "    (l_0): Sequential(\n",
       "      (bl_0): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (idconv): ConvLayer(\n",
       "          (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_1): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_2): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "          (sa): SimpleSelfAttention(\n",
       "            (conv): Conv1d(256, 256, kernel_size=(1,), stride=(1,), bias=False)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "    )\n",
       "    (l_1): Sequential(\n",
       "      (bl_0): NewResBlock(\n",
       "        (reduce): MaxBlurPool2d()\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (idconv): ConvLayer(\n",
       "          (conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_1): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_2): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_3): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "    )\n",
       "    (l_2): Sequential(\n",
       "      (bl_0): NewResBlock(\n",
       "        (reduce): MaxBlurPool2d()\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (idconv): ConvLayer(\n",
       "          (conv): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_1): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_2): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_3): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_4): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_5): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "    )\n",
       "    (l_3): Sequential(\n",
       "      (bl_0): NewResBlock(\n",
       "        (reduce): MaxBlurPool2d()\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (idconv): ConvLayer(\n",
       "          (conv): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_1): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_2): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (head): Sequential(\n",
       "    (pool): AdaptiveAvgPool2d(output_size=1)\n",
       "    (flat): Flatten()\n",
       "    (fc): Linear(in_features=2048, out_features=10, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (conv_0): ConvLayer(\n",
       "    (conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "    (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (act_fn): Mish()\n",
       "  )\n",
       "  (conv_1): ConvLayer(\n",
       "    (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "    (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (act_fn): Mish()\n",
       "  )\n",
       "  (conv_2): ConvLayer(\n",
       "    (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "    (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (act_fn): Mish()\n",
       "  )\n",
       "  (stem_pool): MaxBlurPool2d()\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.stem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (l_0): Sequential(\n",
       "    (bl_0): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (idconv): ConvLayer(\n",
       "        (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_1): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_2): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (sa): SimpleSelfAttention(\n",
       "          (conv): Conv1d(256, 256, kernel_size=(1,), stride=(1,), bias=False)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "  )\n",
       "  (l_1): Sequential(\n",
       "    (bl_0): NewResBlock(\n",
       "      (reduce): MaxBlurPool2d()\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (idconv): ConvLayer(\n",
       "        (conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_1): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_2): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_3): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "  )\n",
       "  (l_2): Sequential(\n",
       "    (bl_0): NewResBlock(\n",
       "      (reduce): MaxBlurPool2d()\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (idconv): ConvLayer(\n",
       "        (conv): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_1): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_2): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_3): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_4): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_5): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "  )\n",
       "  (l_3): Sequential(\n",
       "    (bl_0): NewResBlock(\n",
       "      (reduce): MaxBlurPool2d()\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (idconv): ConvLayer(\n",
       "        (conv): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_1): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_2): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.body"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (pool): AdaptiveAvgPool2d(output_size=1)\n",
       "  (flat): Flatten()\n",
       "  (fc): Linear(in_features=2048, out_features=10, bias=True)\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lr find"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /notebooks/data/imagewoof2\n",
      "Learn path /notebooks/data/imagewoof2\n"
     ]
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='0' class='' max='1', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      0.00% [0/1 00:00<00:00]\n",
       "    </div>\n",
       "    \n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table><p>\n",
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='92' class='' max='282', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      32.62% [92/282 00:28<00:58 8.8006]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "set state called\n",
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
     ]
    }
   ],
   "source": [
    "learn.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uXL6VHU2d/OyDF+bEzOWRqKUgInIc2w618+M/7+TtdTO5ZGFuL7WjpCAiMgp354sPPk9pYT6fvSr3V+FRUhARGcXDGxt4cvthPn3lIqaUF6U7nNApKYiIjKCrN8Y/PrSJM6dX8q4LT0t3OCmhpCAiMoLvPr6dA6093H7t2RTkT4yvy4lRSxGRE7SzuZMfrtzJW5fOoG7O5HSHkzJKCiIiw/jBn3ZQkG987s2537k8mJKCiMgQsXiCR59v4I1nTsvJpSxGo6QgIjLE6l0tHOnq482LT0l3KCmnpCAiMsQjGxsojuTx+kW5PVFtOEoKIiKDJBLOIxsbuHThVEoLJ95KQEoKIiKDPLf3CI3tUd58zsQ7dQRKCiIiL/HwhgYK8/O4/Iyp6Q4lLZQUREQC7s7DGxt47YIaKopzdyXU0SgpiIgENu5vY//Rbq6agKOO+ikpiIgEHt54kPw8401nTkt3KGmjpCAiQvLU0SMbG7j49ClMKitMdzhpo6QgIgJsPdTBi82dE/rUESgpiIgA8B9r92MGV5w9cU8dgZKCiAhdvTHuXb2HK86axtSKibXW0VChJQUzm2Vmj5vZZjN73sw+PkK5S81sbVDmT2HFIyIykl/X7+NoVx83X3J6ukNJuzDncMeAT7n7s2ZWAawxs+Xuvqm/gJlVA98DrnL3PWY2MWeLiEjaxBPOj/78IuefVs2y2RPnugkjCa2l4O4H3f3ZYLsd2AzMGFLsXcBv3H1PUK4xrHhERIbz6PMN7G3pVishkJI+BTObAywFVg15aCEwycxWmNkaM3vvCM+/2czqzay+qakp3GBFZMJwd36w8kXmTCnlTWdN7FFH/UJPCmZWDjwAfMLd24Y8XAAsA/4CuBL4BzNbOPQ13P0ud69z97ra2om3lK2IhOOZXUdYt/coH3rd6eTnWbrDyQihrgtrZhGSCeEed//NMEX2Ac3u3gl0mtlK4Dxga5hxiYgA3LXyRSaVRrjx/JnpDiVjhDn6yIAfA5vd/ZsjFPsP4HVmVmBmpcCrSPY9iIiEantjB49tPsRNF8+hpDA/3eFkjDBbCq8BbgI2mNnaYN/ngdMA3P377r7ZzB4B1gMJ4EfuvjHEmEREiMUTfP63GygtzOe9F89OdzgZJbSk4O5/Bo57ks7dvwF8I6w4RESGuvOxraze2cKdf3UeNeVF6Q4no2hGs4hMKCteaOS7j+/gHRfM4vql6ksYSklBRCaMA0e7+eR9aznjlAq+eM3Z6Q4nIykpiMiE0BdP8NFfPEdvLMH33n0+xRF1Lg8n1CGpIiKZ4ruPb2fN7iP8yzuXcnptebrDyVhqKYhIztt6qJ3vPr6da5ecyjXnnZrucDKakoKI5LR4wvnsA+spLyrg1recle5wMp6SgojktJ/9zy6e23OUW//yLKZo+OlxKSmISM7ad6SLbzz6ApcuquW6JUMXaZbhKCmISE5ydz7/2+QCCf943WKSK+/I8SgpiEhO+vmqPazc2sTfXbmImZNK0x1O1lBSEJGc89SOZr70++e5dFEtN108J93hZBUlBRHJKbuaO/nbe55lbk0Z337nUl0n4QQpKYhIzmjr6eN//awegB+9r46K4kiaI8o+SgoikhPiCedjv3iOXc2d/Ou7lzF7Slm6Q8pKWuZCRLJeXzzBJ+9by4oXmvjK9edw8bwp6Q4paykpiEhW6+mL8+F7nuWPWxr5+zefwbtedVq6Q8pqSgoikrU6ozH++mf1PLXjMLdft5ibLtJV1E6WkoKIZKXNB9v4wm83sHbvUb759vN46/m6YM54UFIQkawRjcV5eEMDP396N/W7j1AcyeN77z6fqxZPT3doOUNJQUSywhPbmvjkfWtp7uhlzpRS/s9fnMmNy2ZSXVqY7tByipKCiGS8e1ft4R/+YyPza8u586+W8Jp5NeRpUloolBREJGPFE87XHt7MD5/YyaWLavn2O5dqQlrIlBREJCP19MX56C+eY/mmQ7zv4tn8w1vOoiBf823DpqQgIhmnL54YmHvwf//yLD7wmrnpDmnCUFIQkYwSTzi3/Godf9zSqLkHaRBaW8zMZpnZ42a22cyeN7OPj1L2AjOLm9mNYcUjIpnP3fnCbzfw4LoDfO7NZyghpEGYLYUY8Cl3f9bMKoA1Zrbc3TcNLmRm+cAdwKMhxiIiGS6RcL78X5v55TN7+chl8/mb189Ld0gTUmhJwd0PAgeD7XYz2wzMADYNKfpR4AHggrBiEZHM9mJTB597YAOrd7Xw/lfP4VNXLEx3SBNWSvoUzGwOsBRYNWT/DOB64HJGSQpmdjNwM8Bpp2mxK5FcEYsn+OETO7nzsa0UF+Tx9RvP5W3LZup6ymkUelIws3KSLYFPuHvbkIf/Gfisu8dH+0/g7ncBdwHU1dV5WLHK2CQSTmN7lOrSCMWR/HSHI1mof7mKH6x8kc0H27jq7FO47dqzmVpZnO7QJrxQk4KZRUgmhHvc/TfDFKkDfhkkhBrgajOLufvvwowrbN29cVq6enF3asqLRvzidHdau/toaOvhUFuUI529tPX00dbdR1tPjOlVxbx+YS1za8rG9MspGovT1h2jraePjp4YxZF8KksKqCqJUBLJp607xv6j3Rw42k1je5Qp5YXMnlLKaZNLKS0swN3piMZo6ezlaFcfBflGcSSf4kg+fbEEq3e28MT2Zp7a3szhzl4AppQVckpVMdMqi6kuiVBZEqGyuIBJZYXMqC5h1uRSZk0upbyogHjC6eyN0RmNEcnPo6a8aFz/7pL59h3p4t5Ve7jvmb0c7uzl9Joyvvfu87n6HK1dlClCSwqW/Bb7MbDZ3b85XBl3nzuo/L8BD2V6Qmjt6uOZXS08s6uFHU0ddEbjdPXG6OqN094T40hXL9FY4iXPKSvMp6aiiJJIPr3xBL2xBH3xBK3dffT0JYY9TmFBHr3B68ycVMLrF9ZSVRKhuSNKc0cvhzuitPfE6O6L09Ubp7s3Tm98+NcCyDNIjNLGqi6N0NUbHzjmSKZWFPH6hbWcN6uatu4+Drb1cPBoNw2tPWw91E5rdx/tPbGXPa+oIO9lf5fZU0p51dzJXDh3CstmT2LWpBJNTspizR1RfrFqD7GEs+iUChZOq2D2lFK2Hepg+aZDPLb5EBv2t5Jn8IYzp/Hei2druYoMFGZL4TXATcAGM1sb7Ps8cBqAu38/xGOPSTQWp6c3QU8sTrQv+SW983Anu5o72dncSUNrDwBmydvhjl5eONSOOxTm53F6bRkVxQVUlRYyvSqf8uICJpcVUl0aYXJpIWbQ3NE78EUe7YtTWJCXvOXnUVFcwLTKYk6pKuaUymImlxVSVRKhojhCYUEeew538adtTfzphSZ+99x+orEEU8oLqSkvoqa8iFmTSyktzKckkk9JYQEVxQVUFhdQWRKhvKiAaCxZp+QXdR+TSpO/3qdXl1BbUcThjii7D3exp6WLg63dlBUVMKWskMllRVSXRIglnGjwt3GcpadNYsHU8uO2WuIJ50hXL/uOdLPvSBd7W7pp6YxSWlhAeVEBZUUFdEZjrN7Vwh82HeJX9fuA5N90Tk0p82rLmTW5lIqiAsqLjz0nkp/820XyjaKCfIojeQP/RvLzyDMjP8/IMwbKS/ga23v44coX+fnTe+iJxQHw4AdI/48RM1g6q5rPXLmIa5ecysxJpWmMWEZj7tl1ir6urs7r6+tP6jV6+uJ85b828/Ond4/463lGdQnTq4qx4D+1u1NeHKFu9iQunDuZJbOqU3o+PZ5wDHLuV1Ui4WxtbGf93lZ2NHewo7GTF5s62H+0+2UtixORZzC9qoSZk5KnsOZMSSab02vLmT2lVH0h4yAWT/BPy7fykz/vpC+e4LolM/jby+Yzo7qEHU0dvNDQzo6mDmZPKeWyM6YytUL9BelkZmvcve645SZaUth0oI2P//I5tjV28I4LZrFgWgXFkTyKC/IpKypgbk2ZvjQyRG8sQWc0RntPjK6+GH0xpzcepzcWtGBiCXr6ki2ZvkRiIHnHE05LZy97W7rYe6SbvS1dNLZHB143z2DB1AqWzZnEBXMmUTd7MjMnlWjEywlo6+njw/c8yxPbmnnr0hl87A0LmFNTlu6wZBRjTQoTZpmLRML5yZM7+fojL1BVGuFnH7yQSxbWpjssGUXyVFshk8pOfr38zmiMnc2d7GjqYEdTJ+v2HuXBtQe4d9UeAGrKC1k8o4pzZ1SxeEYVhQV5dAQJqTMaY0Z1CWefWsWsyUoeew538aG7n2Fncyd33HAOf3WBhonnkgmTFO6r38s//udm3nTWNO644Vwmj8MXjWSPsqICFgdf+P3iCWfroXbqd7Wwfl8rG/a3snJr06gd8hXFBZw5vZLaiiLKCpOty/KiAooj/X07yT6Owf0nFcUFzJpUSmFBdvdxxOIJntjezKd+tY54wvnZhy7k1fNq0h2WjLMJkxRuOH8m5UUFvOXc6RP+l54k5ecZZ06v5MzplQP7unvjbGloI+HJBFBRXEBJJJ/dh7t4/kAbzx9oZUtDO1sOttEZjdMRjdHZG+N4Z2EL8ox5teUsOqWCRadUMD0YXDA1GGhQXpSZH8XGth4e29zIyq1NPLmjmfaeGHNryvjx++o4vbY83eFJCCZcn4LIeHP3gf6N7r7k8OCu3jidQcI42tXHtsZkx+sLDe3sP9r9steYVlnE/KnlzK8tZ97UcmZUl3BqcKssLhiXHzLdvXEe3niQ5o4or55Xw1nTK0ccuLCloY27Vr7I79ceIJZwTq0q5pKFtbxuQS2XLqqlLEOTmIxMfQoiKWJ2bJJf9RjKd0ZjHAomLB5q62H/0e5kX0djB/ev2Udnb/wl5aeUFXLDspm851WzOW3KiQ/l3HSgjV8+s4ffPrf/JXNIJpcV8up5UzhzeiWRfCM/L498gxVbm1jxQhMlkXzec9Fs3v2q05g/hqHIkhvUUhDJIO7JJUT2H+3m4NEeDhztZs3uIyzffIiEO69fWMvVi6fT3RfncGcvLZ1R4gk4+9RKzptZzaJTKjCDNbuP8PiWRv57SyPbGjsoLMjj6sWn8I4LT+P0mjKe3NHME9uSt6ZBI7MgmYTe/+o5vOei2ePSyS+ZQUNSRXJIQ2sP967ewy9W7xn4EjeD6pIICYfW7j4gOQGwf+RUJN+4cO5k3nTmNK5bOoPq0pd/wfef+oonnFjc6UskqAwmT0puUVIQyUF98QS7D3dRXRqhuiRCQX4e7s6+I92s23eUDfta6YjGeN2CWl67oCZjO7Al9dSnIJKDIvl5zJ/60lE/Zjaw8OBbzj01TZFJrlAbUUREBigpiIjIACUFEREZoKQgIiIDlBRERGSAkoKIiAxQUhARkQFKCiIiMiDrZjSbWROwe5iHqoDWV3i/f7v/3xqg+RWGOPQ4J1JmuP1jiXvw9uB9YdYjzDoM3p7o70W66zB4O1PeC322X1k9Zrv78a8s5u45cQPueqX3+7cH/Vs/XnGcSJnh9o8l7uHqEHY9wqyD3ovMqUMmvhf6bJ9cPY53y6XTRw+exP0HRygzHnGcSJnh9o8l7sHb41GHsbxOmHUYy/HHIhfei3TXYawxHM941kOf7RBl3emjVDCzeh/DwlGZLhfqkQt1gNyoh+qQOcKsRy61FMbTXekOYJzkQj1yoQ6QG/VQHTJHaPVQS0FERAaopSAiIgOUFEREZEDOJwUz+4mZNZrZxlfw3GVmtsHMtpvZv9igK5eb2UfN7AUze97Mvj6+Ub8sjnGvg5l90cz2m9na4Hb1+Ef+slhCeS+Cxz9tZm5mNeMX8bBxhPFe3G5m64P34Q9mFvqVckKqxzfMbEtQl9+aWfX4R/6SOMKow9uCz3TCzELrkD6Z2Ed4vfeZ2bbg9r5B+0f93AwrrLGumXIDLgHOBza+gueuBi4GDHgYeHOw/zLgMaAouD81C+vwReDT2f5eBI/NAh4lOamxJtvqAFQOKvMx4PvZ+F4AVwAFwfYdwB1ZWIczgUXACqAu02IP4pozZN9k4MXg30nB9qTR6jnaLedbCu6+EmgZvM/M5pnZI2a2xsyeMLMzhj7PzKaT/LD+jyf/uj8Drgse/t/A19w9GhyjMQvrkHIh1uNO4O+A0EdNhFEHd28bVLSM7K3HH9w9FuW9+4EAAAXFSURBVBR9GpiZhXXY7O4vhBn3ycQ+giuB5e7e4u5HgOXAVa/085/zSWEEdwEfdfdlwKeB7w1TZgawb9D9fcE+gIXA68xslZn9ycwuCDXa4Z1sHQA+EjT1f2Jmk8ILdVQnVQ8zuwbY7+7rwg50FCf9XpjZl81sL/Bu4NYQYx3NePyf6vdBkr9MU20865BqY4l9ODOAvYPu99fnFdWzYIwHzRlmVg68Gvj1oNNrRcMVHWZf/y+4ApLNtIuAC4BfmdnpQTYO3TjV4V+B24P7twP/RPKDnDInWw8zKwW+QPK0RVqM03uBu38B+IKZ/T3wEeD/jnOooxqvegSv9QUgBtwznjEez3jWIdVGi93MPgB8PNg3H/gvM+sFdrr79Yxcn1dUzwmXFEi2jo66+5LBO80sH1gT3P09yS/Nwc3fmcCBYHsf8JsgCaw2swTJBaqawgx8kJOug7sfGvS8HwIPhRnwCE62HvOAucC64IM0E3jWzC5094aQY+83Hv+fBrsX+E9SnBQYp3oEnZxvAd6Qqh9Jg4z3e5FKw8YO4O4/BX4KYGYrgPe7+65BRfYBlw66P5Nk38M+Xkk9w+pIyaQbMIdBHTrAU8Dbgm0Dzhvhec+QbA30d9JcHez/G+C2YHshyaabZVkdpg8q80ngl9n4Xgwps4uQO5pDei8WDCrzUeD+bHwvgKuATUBtKuIP8/8TIXc0v9LYGbmjeSfJsxeTgu3JY6nnsHGl6s1L1w34BXAQ6COZOT9E8tflI8C64D/xrSM8tw7YCOwAvsOxGeCFwM+Dx54FLs/COvw7sAFYT/LX0/Qw6xBWPYaU2UX4o4/CeC8eCPavJ7no2YxsfC+A7SR/IK0NbqGOogqpDtcHrxUFDgGPZlLsDJMUgv0fDP7+24EPnMjnZuhNy1yIiMiAiTr6SEREhqGkICIiA5QURERkgJKCiIgMUFIQEZEBSgqSE8ysI8XH+5GZnTVOrxW35AqpG83sweOtLmpm1Wb2t+NxbJGhNCRVcoKZdbh7+Ti+XoEfW9wtVINjN7O7ga3u/uVRys8BHnL3xamITyYWtRQkZ5lZrZk9YGbPBLfXBPsvNLOnzOy54N9Fwf73m9mvzexB4A9mdqmZrTCz+y15nYB7+tejD/bXBdsdwYJ268zsaTObFuyfF9x/xsxuG2Nr5n84tthfuZn90cyeteSa+NcGZb4GzAtaF98Iyn4mOM56M/vSOP4ZZYJRUpBc9i3gTne/ALgB+FGwfwtwibsvJbki6VcGPedi4H3ufnlwfynwCeAs4HTgNcMcpwx42t3PA1YCfz3o+N8Kjn/cNWeCNXreQHKGOUAPcL27n0/yGh7/FCSlzwE73H2Ju3/GzK4AFgAXAkuAZWZ2yfGOJzKcibggnkwcbwTOGrTqZKWZVQBVwN1mtoDkqpGRQc9Z7u6D17lf7e77AMxsLcn1av485Di9HFtQcA3wpmD7Yo6tX38v8P9GiLNk0GuvIbkePiTXq/lK8AWfINmCmDbM868Ibs8F98tJJomVIxxPZERKCpLL8oCL3b178E4z+zbwuLtfH5yfXzHo4c4hrxEdtB1n+M9Mnx/rnBupzGi63X2JmVWRTC4fBv6F5LUVaoFl7t5nZruA4mGeb8BX3f0HJ3hckZfR6SPJZX8geW0CAMysf1niKmB/sP3+EI//NMnTVgDvOF5hd28leTnOT5tZhGScjUFCuAyYHRRtByoGPfVR4IPBmvyY2QwzmzpOdZAJRklBckWpme0bdLuF5BdsXdD5uonkkucAXwe+amZPAvkhxvQJ4BYzWw1MB1qP9wR3f47kKpnvIHmRmjozqyfZatgSlDkMPBkMYf2Gu/+B5Omp/zGzDcD9vDRpiIyZhqSKhCS4Mly3u7uZvQN4p7tfe7zniaST+hREwrMM+E4wYugoKb7cqcgroZaCiIgMUJ+CiIgMUFIQEZEBSgoiIjJASUFERAYoKYiIyID/D+7yyUePxgQNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# epochs 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /notebooks/data/imagewoof2\n",
      "Learn path /notebooks/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.880600</td>\n",
       "      <td>1.861133</td>\n",
       "      <td>0.412064</td>\n",
       "      <td>0.889030</td>\n",
       "      <td>01:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.641007</td>\n",
       "      <td>1.565336</td>\n",
       "      <td>0.545686</td>\n",
       "      <td>0.931789</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.482279</td>\n",
       "      <td>1.355628</td>\n",
       "      <td>0.645202</td>\n",
       "      <td>0.946806</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.336290</td>\n",
       "      <td>1.240822</td>\n",
       "      <td>0.696615</td>\n",
       "      <td>0.956223</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.093125</td>\n",
       "      <td>1.069190</td>\n",
       "      <td>0.776533</td>\n",
       "      <td>0.974803</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /notebooks/data/imagewoof2\n",
      "Learn path /notebooks/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.879902</td>\n",
       "      <td>1.819809</td>\n",
       "      <td>0.431407</td>\n",
       "      <td>0.885722</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.656415</td>\n",
       "      <td>1.542313</td>\n",
       "      <td>0.560448</td>\n",
       "      <td>0.933062</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.469006</td>\n",
       "      <td>1.351075</td>\n",
       "      <td>0.638839</td>\n",
       "      <td>0.949096</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.335581</td>\n",
       "      <td>1.270019</td>\n",
       "      <td>0.681598</td>\n",
       "      <td>0.959277</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.101048</td>\n",
       "      <td>1.067657</td>\n",
       "      <td>0.779588</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>01:42</td>\n",
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     "metadata": {},
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   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /notebooks/data/imagewoof2\n",
      "Learn path /notebooks/data/imagewoof2\n"
     ]
    },
    {
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       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
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   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /notebooks/data/imagewoof2\n",
      "Learn path /notebooks/data/imagewoof2\n"
     ]
    },
    {
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   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /notebooks/data/imagewoof2\n",
      "Learn path /notebooks/data/imagewoof2\n"
     ]
    },
    {
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      ]
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   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9YxX1amby0zO6cO1x7alTQ+NZJbmV5y90PPCKmT1FdLrvm4BxB95FpOpydyYt3cQLk5bz7rx1ZFYzhp3YkZtP6kSjOvp+JamhPEni58CNwM1E60S8CzwbRlAiqWxXfiGvT1/FqE+XsWj9ThrVqc71J3Tg2mM70LKhiv5IainPYLpioqOunwwvHJHUtWTDTl6YtJx/TctjR34h3Vs34KELe/K9XodpfiVJWeUZJ5ED3A90A776OuTuHUOISyQlFBU7E+evZ9SkZfx30UaqZxhn9zyMK49pR5+2jXQrq6S88nQ3PQfcAzwKnEJ0Hif9D5Aqacuufbycu5IXJi1n1dY9tGxQi7sGdeaSo7NpVl9zK0n6KE+SqO3u7wd3OC0Hfmtm/yWaOESqhNl52xg5aRljZ65mX2Exx3Rsyq/OOoKB3VqQmaHbWCX9lCdJ7DWzasCioNrcKqB5OGGJJI/8wiLenr2GUZOWM33FVurUyODiSBuuOqY9nVtoXiVJb+VJEj8G6gC3AfcR7XK6+mA7mdlg4DGi5UufdfcHSmlzMfBborfWznT3y4L1VwO/Cpr93t1HliNeke9k9dY9vDR5BaOnrGDTrn10zKrLPd/rxgVHtaGBajlIFRFXkggGzl3s7j8FdhK9HhHvfk8AA4E8YKqZjY2tVR1cEL8bOC4Yxd08WN+EaFdWhGjymBbsuyXuoxMpp5JjG4rdOa1rC64+th3HdcrStBlS5cSVJNy9yMyOih1xHad+wGJ3XwpgZmOAc4F5MW1uAJ7Y/+Hv7uuD9WcAE9x9c7DvBGAwMLocry8Sl9LGNtxwQkcu75+tOg5SpZWnu2k68GZQlW7X/pXu/voB9mkNrIxZzgP6l2jTGcDMPiHaJfVbdx9Xxr6tS76AmQ0DhgFkZ2fHeywigMY2iBxMeZJEE2ATcGrMOgcOlCRKOzcveSaSCeQQLVrUBvivmXWPc1/cfTgwHCASiZTnLEeqKI1tEIlfeUZcx3UdooQ8oG3MchtgdSltPgvKn35pZguIJo08vlntrg3w4SHEIAJobIPIoSjPiOvnKP2b/HUH2G0qkGNmHYjeMjsUuKxEmzeI1sx+3syyiHY/LQWWAH8M6lYADCJ6gVukXDS2QeTQlae76a2Yx7WA8/j2WcE3uHthMKZiPNHrDSPcfa6Z3QvkuvvYYNsgM5sHFAE/dfdNAGZ2H9FEA3Dv/ovYIgejsQ0iFcPKd7NSzI7RgXXvufupB21cSSKRiOfmqtx2VVba2IYrj2mnsQ0iB2Bm08oqQ/1dKp7kALqdSBJOYxtEwlOeaxI7+OY1ibVEa0yIJITGNoiErzx3N6kjV5KCxjaIVJ7ynEmcB0x0923BciPgZHd/I6zgRPbT2AaRxCjPNYl73P3f+xfcfauZ3UP0FlaRUGhsg0hilSdJlHZD+Xe58C1SppJjGwZ0bKKxDSIJUJ4P+Vwz+zPRWV0d+BEwLZSopEoqa2zDlQPa06WlLomJJEJ5ksSPgF8DLwfL7/J1rQeRQ6a6DSLJqzx3N+0CfhFiLFKFlDW24apj2nH84RrbIJIsynN30wTgInffGiw3Bsa4+xlhBSfpp7SxDdef0IEr+rfT2AaRJFSe7qas/QkCILaKnMjBaGyDSGoqT5IoNrNsd18BYGbtKWVWWJH9ShvbcFaPVlx1bHuNbRBJEeVJEr8E/mdmHwXLJxJUhBOJpbENIumjPBeux5lZhGhimAG8CewJKzBJPRrbIJJ+ynPh+nrgdqIV4mYAA4BJfLOcqVQxGtsgkt7K0910O3A00VKjp5hZV+B34YQlyW7Ntj28+JnGNoiku/Ikib3uvtfMMLOa7j7fzLocbCczGww8RrQy3bPu/kCJ7dcADxEtbwrwuLs/G2wrAmYH61e4+znliFdC8vbsNfz45RkUFBVrbINImitPksgLZn59A5hgZls4SPlSM8sgOo3HQCAPmGpmY919XommL7v7raU8xR53712OGCVkz3/yJb97ax592jbisaF9NLZBJM2V58L1ecHD35rZB0BDYNxBdusHLHb3pQBmNgY4FyiZJCTJFRc7D45fwFMfLWFgtxb87dI+Gt8gUgUc0i0n7v6Ru491930HadoaWBmznBesK+kCM5tlZq+ZWduY9bXMLNfMPjOz7x9KrPLd7Sss5s5XZ/LUR0u4vH82T11xlBKESBUR9lTfpXVSlxyA93/AaHfPN7ObgJF8fcdUtruvNrOOwEQzm+3uS77xAmbDCMZrZGer5HZF25lfyM3/nMZ/F23kzoGdufXUwzUITqQKCfvm9Twg9sygDSWuY7j7JnfPDxafAY6K2bY6+L0U+BDoU/IF3H24u0fcPdKsWbOKjb6KW79jL5c8PYlPl2ziwQt78qPTcpQgRKqYsJPEVCDHzDqYWQ1gKDA2toGZtYpZPAf4Iljf2MxqBo+zgOPQtYxKs3TDTi548lOWbtjFs1dFuDjS9uA7iUjaCbW7yd0LzexWYDzRW2BHuPtcM7sXyHX3scBtZnYOUAhsBq4Jdj8CeNrMiokmswdKuStKQjB9xRZ+MDIXgNHDBtC7baMERyQiiWLu6TNHXyQS8dzc3ESHkdLe/2Idt7z0Oc3r12LUdf1on1U30SGJSMjMbJq7R0rbphrV8pUxU1bwyzfm0K1VA0Zcc7Qm4xMRJQmJVon76/uLefS9hZzYuRlPXt6XujX1pyEiShJVXmFRMb9+cw6jp6zk/L6t+dMFPamuGVtFJKAkUYXt2VfEj0Z/zntfrOeWUzpx16AuusVVRL5BSaKK2rxrHz8YOZUZK7dy77lHctUx7RMdkogkISWJKmjl5t1cPWIKeVv38OTlfRncvdXBdxKRKklJooqZs2ob1z4/lfyCIl68vj9Ht2+S6JBEJIkpSVQh/1u0kZv+OY0GtTJ56eZjyWmhynEicmBKElXEG9NXcderMzm8eT2ev7YfLRvWSnRIIpIClCTSnLsz/OOl3P/OfAZ0bMLTV0ZoWFvlRUUkPkoSaay42LnvP/N47pNlnNWzFX++uBc1M1UHQkTipySRpvYWFHHnKzP5z+w1XHdcB3511hGqQS0i5aYkkYa27Slg2KhcJn+5mf93ZlduOKGjBsmJyCFRkkgza7bt4ZoRU1m6cSePDe3Nub1LqxYrIhIfJYk0snDdDq4eMYUdewt5/tp+HHd4VqJDEpEUpySRJqZ8uZnrR06lZvUMXr5xAEce1jDRIYlIGgh9uk8zG2xmC8xssZn9opTt15jZBjObEfxcH7PtajNbFPxcHXasqWrcnDVc8Y/JZNWvyes3H6sEISIVJtQzCTPLAJ4ABgJ5wFQzG1tKGdKX3f3WEvs2Ae4BIoAD04J9t4QZc6oZNWkZ94ydS++2jfjH1UfTpG6NRIckImkk7DOJfsBid1/q7vuAMcC5ce57BjDB3TcHiWECMDikOFOOu/PguPn85s25nNa1BS9dP0AJQkQqXNhJojWwMmY5L1hX0gVmNsvMXjOztuXct8opKCrmzldn8vcPl3Bpv2yeuqIvtWtokJyIVLywk0RpN+d7ieX/A9q7e0/gPWBkOfbFzIaZWa6Z5W7YsOE7BZsKduYX8oORubz++SruGNiZP57XnUxVkhORkIT96ZIHtI1ZbgOsjm3g7pvcPT9YfAY4Kt59g/2Hu3vE3SPNmjWrsMCT0YYd+Vw6/DM+WbyRP13Qg9tOy9EgOREJVdhJYiqQY2YdzKwGMBQYG9vAzGIr3pwDfBE8Hg8MMrPGZtYYGBSsq5K+3LiLC578lEXrd/DMVUdxydHZiQ5JRKqAUO9ucvdCM7uV6Id7BjDC3eea2b1ArruPBW4zs3OAQmAzcE2w72Yzu49oogG41903hxlvspqxcivXPR/9Zxh9wwD6ZDdOcEQiUlWY+7e6+VNWJBLx3NzcRIdRoSbOX8ctL04nq34NRl7bj47N6iU6JBFJM2Y2zd0jpW3TiOsk9srUldz979kc0ao+z13Tj2b1ayY6JBGpYpQkkpC787eJi/nzhIWckJPFk1ccRb2aeqtEpPLpkyfJFBYV85uxc3lp8grO79OaBy7oSY1M3eIqIomhJJFE9uwr4kejp/PeF+u4+eRO/OyMLrrFVUQSSkkiSWzZtY8fjJzK9JVb+d05R3L1se0THZKIiJJEMli5eTdXPzeFvC17eOKyvpzZo9XBdxIRqQRKEgk2d/U2rnluKvkFRbxwXT/6d2ya6JBERL6iJJFAnyzeyI0vTKN+rUxevPlYOreon+iQRES+QUkiQd6csYq7Xp1Jx6x6PH/d0bRqWDvRIYmIfIuSRAI88/FS/vD2F/Tr0IRnrorQsHb1RIckIlIqJYlKVFzs/OHtL/jH/77krB6teOTiXtSqrjoQIpK8lCQqSX5hEXe+MpO3Zq3hmmPb85uzu1GtmsZAiEhyU5KoBNv3FjBsVC6fLd3M3UO6MuzEjhokJyIpQUkiZGu37eWa56aweP1OHr2kF+f1aZPokERE4qYkEaLF63dw1T+msG1PAc9dezQn5KR35TwRST9KEiGZumwz14/MpXpGNV6+8Ri6t26Y6JBERMpNSSIE4+as5fYx0zmsUW1GXdePtk3qJDokEZFDEvoc1GY22MwWmNliM/vFAdpdaGZuZpFgub2Z7TGzGcHPU2HHWhFemLSMm1+cxhGtGvCvm49VghCRlBbqmYSZZQBPAAOBPGCqmY1193kl2tUHbgMml3iKJe7eO8wYK4q78/C7C3jigyWc1rU5j1/Wl9o1NAZCRFJb2GcS/YDF7r7U3fcBY4BzS2l3H/AgsDfkeEJRUFTMXa/O4okPljD06LY8feVRShAikhbCThKtgZUxy3nBuq+YWR+grbu/Vcr+Hcxsupl9ZGYnhBjnIduVX8j1I3P51+d5/Pj0HO4/vweZGaokJyLpIewL16WNGPOvNppVAx4Friml3Rog2903mdlRwBtmdqS7b//GC5gNA4YBZGdnV1Tccdm4M5/rnp/KnFXbuP/8Hlzar3JfX0QkbGF/5c0D2sYstwFWxyzXB7oDH5rZMmAAMNbMIu6e7+6bANx9GrAE6FzyBdx9uLtH3D3SrFnljUNYtnEXFzz5KQvX7WD4lRElCBFJS2GfSUwFcsysA7AKGApctn+ju28DsvYvm9mHwF3unmtmzYDN7l5kZh2BHGBpyPHGZebKrVz3/FSK3XnphgH0zW6c6JBEREIRapJw90IzuxUYD2QAI9x9rpndC+S6+9gD7H4icK+ZFQJFwE3uvjnMeOPxwYL1/PCfn9O0Xg1GXtePTs3qJTokEZHQmLsfvFWKiEQinpubG9rzv5K7krtfn03XlvV57tqjaV6/VmivJSJSWcxsmrtHStumEddxcHcen7iYRyYs5PjDs3jyir7Ur6VCQSKS/pQkDqKo2Lln7Bz++dkKzuvTmj9d0JMambrFVUSqBiWJA9hbUMRto6fz7rx13HhSR35+RlcVChKRKkVJogxbd+/jByNz+XzFFu75XjeuPa5DokMSEal0ShKlyNuym6tHTGHl5j08fmlfzurZKtEhiYgkhJJECfNWb+ea56awp6CIUT/ox4COTRMdkohIwihJxPh08UZufGEadWtm8tpNx9KlZf1EhyQiklBKEoGxM1dz5yszaN+0LiOv68dhjWonOiQRkYRTkiBaavS20dPp174Jz1wVoWEdjYEQEQElCQAi7Rpz37lHclGkLbWqqw6EiMh+ShKAmXHlMe0THYaISNLR0GERESmTkoSIiJRJSUJERMqkJCEiImVSkhARkTIpSYiISJmUJEREpExpVb7UzDYAyxMdxyHKAjYmOogQ6LhSi44rtVTUcbVz92albUirJJHKzCy3rBqzqUzHlVp0XKmlMo5L3U0iIlImJQkRESmTkkTyGJ7oAEKi40otOq7UEvpx6ZqEiIiUSWcSIiJSJiWJSmRmGWY23czeCpY7mNlkM1tkZi+bWY1gfc1geXGwvX0i4z4YM2tkZq+Z2Xwz+8LMjjGzJmY2ITi2CWbWOGhrZvbX4NhmmVnfRMdfFjP7iZnNNbM5ZjbazGql4ntmZiPMbL2ZzYlZV+73x8yuDtovMrOrE3Essco4roeCv8NZZvZvM2sUs+3u4LgWmNkZMesHB+sWm9kvKvs4Si4FMTEAACAASURBVCrtuGK23WVmbmZZwXL475e766eSfoA7gJeAt4LlV4ChweOngJuDxz8EngoeDwVeTnTsBzmukcD1weMaQCPgQeAXwbpfAH8KHp8JvAMYMACYnOj4yzim1sCXQO2Y9+qaVHzPgBOBvsCcmHXlen+AJsDS4Hfj4HHjJDyuQUBm8PhPMcfVDZgJ1AQ6AEuAjOBnCdAx+NudCXRLtuMK1rcFxhMdC5ZVWe9Xwv+Aq8oP0AZ4HzgVeCt4UzfG/EEfA4wPHo8HjgkeZwbtLNHHUMZxNQg+TK3E+gVAq+BxK2BB8Php4NLS2iXTT5AkVgb/yTKD9+yMVH3PgPYlPkzL9f4AlwJPx6z/RrtkOa4S284DXgwe3w3cHbNtfPD+ffUeltYumY4LeA3oBSyLSRKhv1/qbqo8fwF+BhQHy02Bre5eGCznEf1ggq8/oAi2bwvaJ6OOwAbguaAr7Vkzqwu0cPc1AMHv5kH7r44tEHvcScPdVwEPAyuANUTfg2mkx3sG5X9/UuJ9K+E6ot+yIcWPy8zOAVa5+8wSm0I/LiWJSmBmZwPr3X1a7OpSmnoc25JNJtFT4yfdvQ+wi2j3RVlS4tiCPvpziXZNHAbUBYaU0jQV37MDKes4Uur4zOyXQCHw4v5VpTRLieMyszrAL4HflLa5lHUVelxKEpXjOOAcM1sGjCHa5fQXoJGZ7a8z3gZYHTzOI9r/SLC9IbC5MgMuhzwgz90nB8uvEU0a68ysFUDwe31M+7Yx+8cedzI5HfjS3Te4ewHwOnAs6fGeQfnfn1R53wgu0p4NXO5BXwupfVydiH5ZmRl8hrQBPjezllTCcSlJVAJ3v9vd27h7e6IXNSe6++XAB8CFQbOrgTeDx2ODZYLtE2P+2JOKu68FVppZl2DVacA8vnkMJY/tquCujAHAtv3dHklmBTDAzOqYmfH1caX8exYo7/szHhhkZo2Ds6xBwbqkYmaDgZ8D57j77phNY4GhwV1oHYAcYAowFcgJ7lqrQfT/59jKjvtA3H22uzd39/bBZ0ge0Df4vxf++5XoCzRV7Qc4ma/vbupI9A91MfAqUDNYXytYXhxs75jouA9yTL2BXGAW8AbRuymaEr1Qvyj43SRoa8ATRO8omQ1EEh3/AY7rd8B8YA7wAtE7Y1LuPQNGE72uUhB8wPzgUN4fon38i4Ofa5P0uBYT7YufEfw8FdP+l8FxLQCGxKw/E1gYbPtlMh5Xie3L+PrCdejvl0Zci4hImdTdJCIiZVKSEBGRMilJiIhImZQkRESkTEoSIiJSJiUJSTlmVmRmM8xsppl9bmbHHqR9IzP7YRzP+6GZpV0d5O/CzJ43swsP3lLSlZKEpKI97t7b3XsRnZDt/oO0b0R0ltakFDOCWyTpKElIqmsAbAEws3pm9n5wdjHbzM4N2jwAdArOPh4K2v4saDPTzB6Ieb6LzGyKmS00sxOCthlBnYKpwZz9NwbrW5nZx8HzztnfPpaZLTOzPwXPOcXMDg/WP29mfzazD4A/WbS+wxvB839mZj1jjum5INZZZnZBsH6QmU0KjvVVM6sXrH/AzOYFbR8O1l0UxDfTzD4+yDGZmT0ePMd/+HriP6mi9A1GUlFtM5tBdJRzK6JzYQHsBc5z9+0WLcrymZmNJTrhYHd37w1gZkOA7wP93X23mTWJee5Md+9nZmcC9xCdw+kHRKc7ONrMagKfmNm7wPlEp5n+g5llAHXKiHd78JxXEZ2z6+xgfWfgdHcvMrO/AdPd/ftmdiowiuhI9l8Hr90jiL1xcGy/CvbdZWY/B+4ws8eJTo/d1d3dvi648xvgDHdfFbOurGPqA3QBegAtiE5FMiKud0XSkpKEpKI9MR/4xwCjzKw70SkK/mhmJxKdkr010Q+6kk4HnvNgbh93j52I7/Xg9zSic/pDdN6bnjF98w2Jzv0zFRhhZtWBN9x9Rhnxjo75/WjM+lfdvSh4fDxwQRDPRDNramYNg1iH7t/B3bdYdFbhbkQ/2CFaLGcSsJ1oonw2OAt4K9jtE+B5M3sl5vjKOqYTgdFBXKvNbGIZxyRVhJKEpDR3nxR8s25GdA6eZsBR7l5g0Rkza5Wym1H2tMn5we8ivv7/YcCP3P1bE6QFCeks4AUze8jdR5UWZhmPd5WIqbT9SovVgAnufmkp8fQjOhnhUOBW4FR3v8nM+gdxzjCz3mUdU3AGpbl65Cu6JiEpzcy6Ei1BuYnot+H1QYI4BWgXNNsB1I/Z7V3gOovO00+J7qbSjAduDs4YMLPOZlbXzNoFr/cM8A+iU6SX5pKY35PKaPMxcHnw/CcDG919exDrrTHH2xj4DDgu5vpGnSCmekBDd38b+DHR7irMrJO7T3b33xCtmLe/DOa3jimIY2hwzaIVcMpB/m0kzelMQlLR/msSEP1GfHXQr/8i8H9mlkt0BtD5AO6+ycw+sWhh+Xfc/afBt+lcM9sHvA38vwO83rNEu54+t2j/zgai1zROBn5qZgXATuCqMvavaWaTiX4p+9a3/8BviVb3mwXs5utpvH8PPBHEXgT8zt1fN7NrgNHB9QSIXqPYAbxpZrWCf5efBNseMrOcYN37ROs4zyrjmP5N9BrPbKIzo350gH8XqQI0C6xIiIIur4i7b0x0LCKHQt1NIiJSJp1JiIhImXQmISIiZVKSEBGRMilJiIhImZQkRESkTEoSIiJSJiUJEREpk5KEiIiUKa2m5cjKyvL27dsnOgwRkZQybdq0je7erLRtaZUk2rdvT25ubqLDEBFJKWa2vKxt6m4SEZEyKUmIiEiZlCRERKRMShIiIlImJQkRESmTkoSIiJQprW6BFRGpStydmXnbeGfOGlrUr8V1x3eo8NdQkhARSSHFxc7nK7bw9uy1jJ+7llVb95BZzbj46LahvJ6ShIhIkissKmbKss2Mm7OWcXPWsn5HPjUyqnFi5yx+MrAzA49oQcM61UN5bSUJEZEkVFBUzKQlm3hnzhrenbuOTbv2Uat6NU7u3JwhPVpyatfm1K8VTmKIpSQhIpIk8guL+GTxRt6evZYJ89axbU8BdWtkcOoRLRjSvSUnd2lGnRqV+7GtJCEikkB7C4r4cMEGxs1Zw/tfrGdHfiH1a2Uy8IgWDOnRihNysqhVPSNh8SlJiIhUsl35hUycv55xc9bywYL17N5XROM61TmzRysG92jJcZ2yqJGZHCMUlCRERCrB9r0FvP/FOt6evZaPF24gv7CYrHo1Oa9Pa87s0Yr+HZqQmZEciSGWkoSISEi27NrHhHnreGfOGv63eCMFRU7LBrW4tF82Q7q3JNK+CRnVLNFhHpCShIhIBdqwI59350VvVf10ySaKip02jWtzzbHtGdy9FX3aNqJakieGWEoSIiLf0dptexk/dy1vz17D1GWbKXbokFWXG0/syJDurejeugFmqZMYYilJiIgcgrwtuxk3Zy3vzFnLtOVbAOjcoh63nprDmT1a0qVF/ZRNDLGUJERE4rRs4y7embOWd+asYVbeNgC6tWrAXYM6M7h7Kw5vXi/BEVY8JQkRkQNYvH4Hb8+OnjF8sWY7AL3aNOQXQ7oypHtL2jWtm+AIw6UkISISw935Ys0Oxs1Zw9tz1rJ4/U4AIu0a86uzjmBw95a0aVwnwVFWHiUJEany3J3Zq7bx9uy1jJuzhmWbdlPNoF+HJlx1zJGccWRLWjSolegwE0JJQkSqpOJiZ/rKLbwTdCWt2rqHjGrGsZ2aMuzETgw6sgVZ9WomOsyEU5IQkSqjqNiZumwz78xew7i5a1m3PTrl9vE5Wfz49BwGdmtBozo1Eh1mUlGSEJG0VlBUzGdLN/HOnLW8O3ctG3fuo2ZmNU7u0owh3Vtx6hHNaVAJU26nKiUJEUk7+YVFfLp4E2/PXsOEL9axdXcBdWpkcErX5pzZvRUnd2lG3Zr6+IuH/pVEJC3sLSjio4UbGDdnLe/NWxedcrtmJqd3a8Hg7i05qXOzhE65naqUJEQkZe3eV8gH8zfw9pw1fDA/OuV2ozrVGdy9JWf2aMWxhzelZqYSw3ehJCEiKWXH3gImzl/P27PX8NHCDewtKKZp3Rp8v09rhnRvyYCOTamehFNupyolCRFJelt3R6fcHjdnLf9dtJF9RcU0r1+TSyJtGdKjFUenwJTbqUpJQkSS0sad+bw7N1qLYdKSTRQWO60b1ebKY9pxZo+W9GnbOKWm3E5VShIikjT2FRYzYd46Rk9ZwadLNlLs0K5pHa4/oSNDurekZ5uGaTGzaipRkhCRhFu5eTejp6zgldyVbNy5j9aNanPLKYczpHsrjmiVHlNupyolCRFJiMKiYt6fv56XJq/g40UbMODUri24vH82J3ZupmsMSUJJQkQq1eqtexgzdSWvTF3J2u17adGgJredmsMlR7flsEa1Ex2elBB6kjCzwcBjQAbwrLs/UGJ7O2AE0AzYDFzh7nnBtmzgWaAt4MCZ7r4s7JhFpGIVFTsfL9zAi5OXM3H+ehw4MacZ9557JKd2bU6mbllNWqEmCTPLAJ4ABgJ5wFQzG+vu82KaPQyMcveRZnYqcD9wZbBtFPAHd59gZvWA4jDjFZGKtX77Xl7JXcnoKStZtXUPWfVqctNJnbi0XzZtm1SdmgypLOwziX7AYndfCmBmY4Bzgdgk0Q34SfD4A+CNoG03INPdJwC4+86QYxWRClBc7HyyZCMvTV7BhHnrKCx2jju8Kf/vzCMY2K0FNTJ11pBKwk4SrYGVMct5QP8SbWYCFxDtkjoPqG9mTYHOwFYzex3oALwH/MLdi0KOWUQOwaad+bw6LY/RU1awfNNuGtepznXHd+DSftl0yErvEp/pLOwkUdrtCV5i+S7gcTO7BvgYWAUUEo3tBKAPsAJ4GbgG+Mc3XsBsGDAMIDs7u+IiF5GDcncmf7mZFyevYNycNRQUOf06NOGOgZ0548iWmlAvDYSdJPKIXnTerw2wOraBu68GzgcIrjtc4O7bzCwPmB7TVfUGMIASScLdhwPDASKRSMkEJCIh2Lp7H//6fBUvTV7Okg27aFArk8v7t+Py/tnktKif6PCkAoWdJKYCOWbWgegZwlDgstgGZpYFbHb3YuBuonc67d+3sZk1c/cNwKlAbsjxikgZ3J3PV2zhxckr+M+sNeQXFtMnuxEPXdiTs3seRu0aOmtIR6EmCXcvNLNbgfFEb4Ed4e5zzexeINfdxwInA/ebmRPtbrol2LfIzO4C3rfocMtpwDNhxisi37Z9bwFvTF/FS5NXMH/tDurVzOSiSBsu69eOboc1SHR4EjJzT58emkgk4rm5OtkQqQiz8rby4mcrGDtzNXsKiujeugGX92/HOb0OU1W3NGNm09w9Uto2vdMi8pVd+YWMnbmaFycvZ86q7dSunsG5vQ/jsv7Z9GzTKNHhSQIoSYgI81Zv56Upy3lj+mp25hfStWV97jv3SM7t05oGtaonOjxJICUJkSpqz74i3pq1mpemrGD6iq3UzKzGWT1bcXn/dvTNbqSZVwVQkhCpchat28GLk1fw+ud5bN9bSKdmdfn12d24oG9rGtWpkejwJMkoSYhUAXsLihg3Zy0vTV7BlGWbqZ5hDOneisv6Z9O/QxOdNUiZlCRE0tiXG3cxesoKXs1dyZbdBbRrWoe7h3TlwqPa0LRezUSHJylASUIkzewvAfri5OV8umQTmdWMgd1acHn/dhzbqanqQku5KEmIpInSSoDeNagzF0fa0rxBrUSHJylKSUIkhakEqIRNSUIkBakEqFQWJQmRFKESoJIIShIiSU4lQCWRlCREkpBKgEqyUJIQSSIqASrJRklCJMFiS4COn7OWfUXFKgEqSUNJQiRBSisBeln/bJUAlaSiJCFSiVQCVFJNXEnCzB4GnnP3uSHHI5KWVAJUUlW8ZxLzgeFmlgk8B4x2923hhSWSHkorAXr/+T1UAlRSRlx/pe7+LPCsmXUBrgVmmdknwDPu/kGYAYqkmtJKgJ7TK1oCtFdblQCV1BL3VxkzywC6Bj8bgZnAHWZ2o7sPDSk+kZShEqCSjuK9JvFn4BzgfeCP7j4l2PQnM1sQVnAiyU4lQCXdxXsmMQf4lbvvLmVbvwqMRyQlqASoVBXxJoktwFfny2bWCDjZ3d/QBWypKlQCVKqieJPEPe7+7/0L7r7VzO4B3ggnLJHkoRKgUpXFmyRKm01M9+9J2lIJUJGoeD/oc4OL108ADvwImBZaVCIJohKgIt8Ub5L4EfBr4GXAgHeBW8IKSqQyqQSoSNniHUy3C/hFyLGIVKqSJUBbNqilEqAiJcQ7TqIZ8DPgSOCrc253PzWkuERCoRKgIuUTb3fTi0S7ms4GbgKuBjaEFZRIRSutBOjNJ3di6NEqASpyIPEmiabu/g8zu93dPwI+MrOPwgxM5LsqqwToL886gtOPUAlQkXjEmyQKgt9rzOwsYDXQJpyQRL4blQAVqTjxJonfm1lD4E7gb0AD4CehRSVSTioBKhKOgyaJYPbXHHd/C9gGnFKeFzCzwcBjQAbwrLs/UGJ7O2AE0AzYDFzh7nkx2xsAXwD/dvdby/Pakv5KKwF6+YBsLuunEqAiFeGgScLdi8zsHODR8j55kGCeAAYCecBUMxvr7vNimj0MjHL3kWZ2KnA/cGXM9vsAXf+Qr5RVAvThi3pxVo9WKgEqUoHi7W761MweJ3qH0679K93984Ps1w9Y7O5LAcxsDHAuEJskuvF119UHxMwHZWZHAS2AcUAkzlglTakEqEjlizdJHBv8vjdmnQMHGyfRGlgZs5wH9C/RZiZwAdEuqfOA+mbWlOjMs48QPas4rawXMLNhwDCA7Ozsg4QjqahkCdAerRuqBKhIJYl3xHW5rkPEKG0+Ay+xfBfwuJldA3wMrAIKgR8Cb7v7ygNNwezuw4HhAJFIpORzS4oqrQToub2jJUB7tlEJUJHKEu+I69+Utt7d7y1tfYw8oG3Mchuit8/GPsdq4PzgdeoBF7j7NjM7BjjBzH4I1ANqmNlOd9f0IGlMJUBFkku85+q7Yh7XIjry+os49psK5JhZB6JnCEOBy2IbmFkWsNndi4G7id7phLtfHtPmGiCiBJGeVAJUJHnF2930SOyymT0MjI1jv0IzuxUYT/QW2BHuPtfM7gVy3X0scDJwv5k50e4mzS5bRagEqEjyM/fyd+ObWWNgirvnVHxIhy4SiXhubm6iw5ADKFkCtEZGNQZ3b8nl/bPppxKgIglhZtPcvdQ7SOO9JjGbry84ZxAd+Haw6xEiX1EJUJHUFO81ibNjHhcC69y9MIR4JI3sLwH60pTlfLI4WgJ00JEtuKyfSoCKpIp4k0QrYK6774DoXUhmdqS7Tw4vNElVX5cAzWPjznxaN6rNT8/owkWRNjSvrxKgIqkk3iTxJNA3Znl3KeukCiuzBOiAbE7MUQlQkVQVb5Iwj7nC7e7FZqahrqISoCJpLt4P+qVmdhvRsweIjoZeGk5Ikgr+t2gjz3+6jInz1+HASZ1VAlQkHcWbJG4C/gr8iuhdTu8TzJckVc+bM1Zx+5gZKgEqUgXEO5huPdHR0lLFzc7bxs9em8XR7Rvzz+v7UzNT03KLpLO4+gXMbKSZNYpZbmxmI8ILS5LRhh35DHshl6Z1a/DkFUcpQYhUAfF2Hvd09637F9x9C9AnnJAkGeUXFnHzP6exZfc+hl8VIUsD4ESqhHiTRLVgKg4AzKwJ8V/PkBTn7tzz5lxyl2/hoQt70b11w0SHJCKVJN4P+keIVqd7LVi+CPhDOCFJsnnhs+WMmbqSW07pxPd6HZbocESkEsV74XqUmU0DTiFaSOj8EnWqJU19umQjv/u/eZzWtTl3DuyS6HBEpJLF3WUUTPG9gWg9Ccws291XhBaZJNzKzbu55cXP6ZBVl78M7a25lkSqoHjvbjrHzBYBXwIfAcuAd0KMSxJsV34hN4zKpajYeeaqCPVVFU6kSor3wvV9wABgobt3AE4DPgktKkmo4mLnzldmsnDdDh6/rC8dsuomOiQRSZB4k0SBu28iepdTNXf/AOgdYlySQH+duIhxc9fy/848ghM7N0t0OCKSQPFek9hqZvWIlhd90czWE60rIWlm3Jy1/OW9RZzftzU/OL5DosMRkQSL90ziXKLTg/8EGAcsAb4XVlCSGPPXbueOV2bQq20j/nheD5USFZG4b4HdFTwsBkaW3G5mk9z9mIoMTCrX5l37uGFULvVqZjL8yqOoVV1TbohIxY2aVrmxFFZQVMwtL37Oum35vHzjAFo00NspIlEVlST84E0kWf3hP18waekmHr6oF32yGx98BxGpMlQdpop7eeoKnv90GT84vgMXHtUm0eGISJKpqCShK5wpaNryzfzqjTmckJPF3UO6JjocEUlC8Y64HlLKuptiFq+ssIikUqzeuocbX/icwxrV5m+X9lHJUREpVbyfDL82s1P3L5jZz4neFguAu8+p6MAkPHsLirjxhWns2VfIs1dFaFSnRqJDEpEkFe+F63OAt8zsp8BgoGuwTlKMu/Pzf81izuptDL8yQk6L+okOSUSSWLzjJDaa2TnAe8A04EJ31x1NKejpj5fy5ozV3DWoMwO7tUh0OCKS5A6YJMxsB9+8vbUG0BG40Mzc3RuEGZxUrA/mr+dP4+ZzVo9W3HLK4YkOR0RSwAGThLvH1RdhZke6+9yKCUnCsGTDTm4bM50jWjbgoYt6asoNEYlLRd3S8kIFPY+EYNueAm4YmUuNjGoMv+oo6tRQeXIRiU9FfVroa2mSKip2bh8znRWbd/Pi9f1p07hOokMSkRSiaTnS3IPj5/Phgg38/vvd6d+xaaLDEZEUE/oIKjMbbGYLzGyxmf2ilO3tzOx9M5tlZh+aWZtgfW8zm2Rmc4Ntl4Qda7p5Y/oqnv5oKZf3z+aKAe0SHY6IpKCKShL7SltpZhnAE8AQoBtwqZl1K9HsYWCUu/cE7gXuD9bvBq5y9yOJjs34i5k1qqB4096svK38/F+z6Ne+Cfd878hEhyMiKSru7iYzOx84nmjX0v/c/d/7t7n7gDJ26wcsdvelwXOMITpSe15Mm25EixkBfAC8ETznwpjnXx1Uw2sGbI035qpq/Y69DBs1jax6Nfn7FX2pkakpN0Tk0MQ7d9PfgZuA2cAc4EYzeyKOXVsDK2OW84J1sWYCFwSPzwPqm9k3Os/NrB/RMRpL4om3KssvLOLmf37O1j37GH7VUWTVq5nokEQkhcV7JnES0H3/KGszG0k0YRxMaXc9lbzIfRfwuJldQ7SG9ipi6mebWSuit9he7e7F33oBs2HAMIDs7Ow4Qkpf7s5v3pjLtOVbePyyPhx5WMNEhyQiKS7efogFQOwncFtgVhz75QVt92sDrI5t4O6r3f18d+8D/DJYtw3AzBoA/wF+5e6flfYC7j7c3SPuHmnWrFmch5OeRn66jJdzV3LrKYdzds/DEh2OiKSBeJNEU+CL4O6jD4leU2hmZmPNbOwB9psK5JhZBzOrAQwFvtHezLLMbH8cdwMjgvU1gH8Tvaj9atxHVEV9ungj9/3nC04/ojl3DOyc6HBEJE3E2930m0N5cncvNLNbgfFABjDC3eea2b1ArruPBU4G7jczJ9rddEuw+8XAiUDToCsK4Bp3n3EosaSzFZt288OXPqdjVl0evaQ31appbKOIVAyLdzJXM2sBHB0sTnH39aFFdYgikYjn5uYmOoxKtTO/kAv+/ilrt+/lzVuOo31W3USHJCIpxsymuXuktG3x3t10MTAFuIjoN/zJZnZhxYUoh6K42LnzlRksWr+Dxy/rowQhIhUu3u6mXwJH7z97MLNmRGtLvBZWYHJwj72/iPFz1/Grs47ghJyqfdFeRMIR74XraiW6lzaVY18JwTuz1/DY+4u4oG8bfnB8h0SHIyJpKt4ziXfMbDwwOli+BHg7nJDkYL5Ys507X51J77aN+MN53VUbQkRCE+/ZgANPAz2BXsDw0CKSA9q8ax83jMqlXs1Mnr7yKGpVz0h0SCKSxuI9kxjo7j8HXt+/wsx+B/w8lKikVAVFxdzy4ues35HPKzceQ4sGtRIdkoikuYPVuL4Z+CHQ0cxiR1jXBz4JMzD5tt+/NY9JSzfxyEW96N1WE+KKSPgOdibxEvAO0em7Y2tB7HD3zaFFJd8yZsoKRk5azvXHd+CCo9okOhwRqSIOmCSCOZS2AZdWTjhSmtxlm/n1m3M4ISeLXwzpmuhwRKQK0W2sSW711j3c9M9ptG5Um8cv7Utmht4yEak8FVXjWkKwZ18Rw17IZW9BMWOGRWhYp3qiQxKRKkZJIkm5Oz//1yzmrt7OM1dGOLx5/USHJCJVkPouktRTHy1l7MzV3DWoC6d3a5HocESkilKSSEIT56/jwfHzObtnK354cqdEhyMiVZiSRJJZvH4nt4+eQbdWDXjowl6ackNEEkpJIols21PAsFG51MisxvCrItSuoSk3RCSxdOE6SRQVO7eNns6Kzbt56YYBtG5UO9EhiYgoSSSLB8fN56OFG/jDed3p16FJosMREQH+f3t3HiVVmd5x/PsTFFlkRwKigIgLo4iAiMuoowbFfT1CmIA6cY0mo8cFo9GMo1FHz+jRcVyjDo6i4h7UMAR1ODFuzc6gCDqorURARBQRWZ78cV+kbLuARqpvFfw+59SpW+99b/Xz9FtdT917q9/rw01l4elJ1dw9/n1+3n8HhuzTOe9wzMy+4yKRs6nVi7jsyWn069qaq4/5Sd7hmJl9j4tEjuYt/oazRkygXbNG3DmkN1t6yg0zKzM+J5GTZStWcs4fJ/DF0uU8ee5+tGnWKO+QzMx+wEUiBxHBlU9PZ+KHi7jj73rTo2PzvEMyM6uVj2/k4MH/ncOoCdVccMhOHNWzQ97hmJkV5SJRz16dvYBrn3+bv+3RngsP2znvcMzM1spFoh598NkSznt4It3aNeWWU3uxxRaecsPMypuLRD35stqYNAAADWJJREFUatkKzhxRBcC9Q/vSrJFPB5lZ+fM7VT1YtSq48LHJvDd/CX84vR+d2zTNOyQzs/XiPYl6cOu4WYyd8SlXHLkbB3Rvm3c4ZmbrzUWixF6cNpfbxs3i5D6dOH3/LnmHY2ZWJy4SJTTjk8Vc9PgU9tqhJdedsLuvDWFmFcdFokQWLvmWM0dU0bxxQ+7+eR8aNfS1Icys8vjEdQksX7mK8x6ewPyvljHq7H3ZtvnWeYdkZrZBvCdRAr8ePYPX31/IDSfuwZ7bt8w7HDOzDVbyIiHpCEkzJc2WNLyW9Z0ljZM0VdIrkjoVrBsmaVa6DSt1rBvDyDc/ZMRrH3DmT7tyYu9O697AzKyMlbRISGoA3AEMBHoAgyX1qNHtZmBERPQErgGuT9u2Bq4G9gH6AVdLalXKeH+st+Ys5Kpnp3Pgzu0YPnC3vMMxM/vRSr0n0Q+YHRHvR8S3wKPAcTX69ADGpeWXC9YfDoyNiIUR8TkwFjiixPFusI8XLeWchybQqVUTbh+0Fw085YaZbQJKXSS2Az4qeFyd2gpNAU5KyycA20hqs57bIuksSVWSqubPn7/RAq+Lpd+u5KwRVSxbsYp7h/ahRZMtc4nDzGxjK3WRqO3jdNR4fDFwkKRJwEHAx8CK9dyWiLgnIvpGRN927dr92HjrLCK45IkpzJi7mNsG92Knbbep9xjMzEql1F+BrQa2L3jcCfiksENEfAKcCCCpGXBSRHwhqRo4uMa2r5Qy2A1x55/fY/TUuVx6xC4csmv7vMMxM9uoSr0n8RbQXVJXSVsBg4DnCjtIaitpdRyXA/en5THAAEmt0gnrAamtbIx7+1NuGjOTY/bsyLkHdcs7HDOzja6kRSIiVgDnk725vw08HhF/kXSNpGNTt4OBmZLeBdoD16VtFwK/Jis0bwHXpLayMHvel/zzo5P5Scfm/Oaknp5yw8w2SYr4wWH+itW3b9+oqqoq+c/54uvlHP/7V/nym+U8e/4BbNeyccl/pplZqUiaEBF9a1vnaTnqaOWq4IJHJ1H9+dc8cmZ/Fwgz26S5SNTRjf/1DuPfnc/1J+7B3l1a5x2OmVlJee6mOnhqYjX3jH+fv+/fmcH9dsg7HDOzknORWE+TP1rE8Kem0X/H1lx1TM2ZRczMNk0uEuth3uJvOPuhKto1a8Tvh/Rhywb+tZnZ5sHnJNbhm+UrOfuPE1i8dAVPnrsfrZtulXdIZmb1xkViLSKCK5+ZzqQPF3HnkN706Ng875DMzOqVj5usxQOvzuGJCdX806HdGbhHh7zDMTOrdy4SRfzPrAVc98LbDOjRnl8e2j3vcMzMcuEiUYs5C5bwj49MpFu7pvz21F5s4WtDmNlmykWihq+WreDMEVVIcN/QvWnWyKdtzGzz5XfAAqtWBRc+Npn3FyxhxBn92KFNk7xDMjPLlfckCtzy3+8ydsanXHnUbuy/U9u8wzEzy52LRPL81Lnc/tJsTunTidP265J3OGZmZcFFAnh77mIuHjWF3ju05NoTdve1IczMEp+TADq2bMxRPTtw6eG70Khhg7zDMTMrGy4SQIvGW3LzKXvmHYaZWdnx4SYzMyvKRcLMzIpykTAzs6JcJMzMrCgXCTMzK8pFwszMinKRMDOzohQRecew0UiaD3yQdxwbqC2wIO8gSsB5VRbnVVk2Vl6dI6JdbSs2qSJRySRVRUTfvOPY2JxXZXFelaU+8vLhJjMzK8pFwszMinKRKB/35B1AiTivyuK8KkvJ8/I5CTMzK8p7EmZmVpSLRD2S1EDSJEmj0+Oukt6QNEvSY5K2Su2N0uPZaX2XPONeF0ktJT0h6R1Jb0vaV1JrSWNTbmMltUp9Jem2lNtUSb3zjr8YSRdK+ouk6ZJGStq6EsdM0v2S5kmaXtBW5/GRNCz1nyVpWB65FCqS103pdThV0tOSWhasuzzlNVPS4QXtR6S22ZKG13ceNdWWV8G6iyWFpLbpcenHKyJ8q6cbcBHwCDA6PX4cGJSW7wLOTcvnAXel5UHAY3nHvo68/gD8Q1reCmgJ/AYYntqGAzem5SOBFwEB/YE38o6/SE7bAX8FGheM1WmVOGbAgUBvYHpBW53GB2gNvJ/uW6XlVmWY1wCgYVq+sSCvHsAUoBHQFXgPaJBu7wE7ptfuFKBHueWV2rcHxpD9L1jb+hqv3F/Am8sN6ASMAw4BRqdBXVDwgt4XGJOWxwD7puWGqZ/yzqFIXs3Tm6lqtM8EOqTlDsDMtHw3MLi2fuV0S0Xio/RH1jCN2eGVOmZAlxpvpnUaH2AwcHdB+/f6lUteNdadADycli8HLi9YNyaN33djWFu/csoLeALYE5hTUCRKPl4+3FR/bgUuBValx22ARRGxIj2uJntjgjVvUKT1X6T+5WhHYD7wQDqUdp+kpkD7iJgLkO63Tf2/yy0pzLtsRMTHwM3Ah8BcsjGYwKYxZlD38amIcavhDLJP2VDheUk6Fvg4IqbUWFXyvFwk6oGko4F5ETGhsLmWrrEe68pNQ7Jd4zsjYi9gCdnhi2IqIrd0jP44skMTHYGmwMBaulbimK1NsTwqKj9JVwArgIdXN9XSrSLyktQEuAK4qrbVtbRt1LxcJOrH/sCxkuYAj5IdcroVaClp9XXGOwGfpOVqsuOPpPUtgIX1GXAdVAPVEfFGevwEWdH4VFIHgHQ/r6D/9gXbF+ZdTg4D/hoR8yNiOfAUsB+bxphB3cenUsaNdJL2aGBIpGMtVHZe3cg+rExJ7yGdgImS/oZ6yMtFoh5ExOUR0SkiupCd1HwpIoYALwMnp27DgGfT8nPpMWn9SwUv9rISEf8HfCRpl9R0KDCD7+dQM7eh6VsZ/YEvVh/2KDMfAv0lNZEk1uRV8WOW1HV8xgADJLVKe1kDUltZkXQEcBlwbER8XbDqOWBQ+hZaV6A78CbwFtA9fWttK7K/z+fqO+61iYhpEbFtRHRJ7yHVQO/0t1f68cr7BM3mdgMOZs23m3Yke6HOBkYBjVL71unx7LR+x7zjXkdOvYAqYCrwDNm3KdqQnaifle5bp74C7iD7Rsk0oG/e8a8lr18B7wDTgYfIvhlTcWMGjCQ7r7I8vcH8YkPGh+wY/+x0O71M85pNdix+crrdVdD/ipTXTGBgQfuRwLtp3RXlmFeN9XNYc+K65OPl/7g2M7OifLjJzMyKcpEwM7OiXCTMzKwoFwkzMyvKRcLMzIpykbCKI2mlpMmSpkiaKGm/dfRvKem89XjeVyRtctdB/jEkPSjp5HX3tE2Vi4RVoqUR0Ssi9iSbkO36dfRvSTZLa1kq+A9us7LjImGVrjnwOYCkZpLGpb2LaZKOS31uALqlvY+bUt9LU58pkm4oeL5TJL0p6V1JP019G6TrFLyV5uw/O7V3kDQ+Pe/01f0LSZoj6cb0nG9K2im1Pyjpt5JeBm5Udn2HZ9Lzvy6pZ0FOD6RYp0o6KbUPkPRaynWUpGap/QZJM1Lfm1PbKSm+KZLGryMnSfpdeo7nWTPxn22m/AnGKlFjSZPJ/su5A9lcWADfACdExGJlF2V5XdJzZBMO7h4RvQAkDQSOB/aJiK8ltS547oYR0U/SkcDVZHM4/YJsuoO9JTUCXpX0J+BEsmmmr5PUAGhSJN7F6TmHks3ZdXRq3xk4LCJWSrodmBQRx0s6BBhB9p/s/5p+9h4p9lYptyvTtkskXQZcJOl3ZNNj7xoRoTUX3LkKODwiPi5oK5bTXsAuwB5Ae7KpSO5fr1GxTZKLhFWipQVv+PsCIyTtTjZFwb9LOpBsSvbtyN7oajoMeCDS3D4RUTgR31PpfgLZnP6QzXvTs+DYfAuyuX/eAu6XtCXwTERMLhLvyIL7WwraR0XEyrR8AHBSiuclSW0ktUixDlq9QUR8rmxW4R5kb+yQXSznNWAxWaG8L+0FjE6bvQo8KOnxgvyK5XQgMDLF9Ymkl4rkZJsJFwmraBHxWvpk3Y5sDp52QJ+IWK5sxsyta9lMFJ82eVm6X8mavw8BF0TEDyZISwXpKOAhSTdFxIjawiyyvKRGTLVtV1usAsZGxOBa4ulHNhnhIOB84JCIOEfSPinOyZJ6Fcsp7UF5rh77js9JWEWTtCvZJSg/I/s0PC8ViJ8BnVO3L4FtCjb7E3CGsnn6qXG4qTZjgHPTHgOSdpbUVFLn9PPuBf6DbIr02pxacP9akT7jgSHp+Q8GFkTE4hTr+QX5tgJeB/YvOL/RJMXUDGgRES8AvyQ7XIWkbhHxRkRcRXbFvNWXwfxBTimOQemcRQfgZ+v43dgmznsSVolWn5OA7BPxsHRc/2HgPyVVkc0A+g5ARHwm6VVlF5Z/MSIuSZ+mqyR9C7wA/Mtaft59ZIeeJio7vjOf7JzGwcAlkpYDXwFDi2zfSNIbZB/KfvDpP/k3sqv7TQW+Zs003tcCd6TYVwK/ioinJJ0GjEznEyA7R/El8KykrdPv5cK07iZJ3VPbOLLrOE8tktPTZOd4ppHNjPrntfxebDPgWWDNSigd8uobEQvyjsVsQ/hwk5mZFeU9CTMzK8p7EmZmVpSLhJmZFeUiYWZmRblImJlZUS4SZmZWlIuEmZkV9f8KVL+hypR5zAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_metrics()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# e5 results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc = np.array([0.776533, 0.779588, 0.770171, 0.791041, 0.776279])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7787224, 0.00687586048724083)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc.mean(), acc.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# epochs 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /notebooks/data/imagewoof2\n",
      "Learn path /notebooks/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.889153</td>\n",
       "      <td>1.805073</td>\n",
       "      <td>0.420209</td>\n",
       "      <td>0.862306</td>\n",
       "      <td>01:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.660659</td>\n",
       "      <td>1.538020</td>\n",
       "      <td>0.554849</td>\n",
       "      <td>0.919318</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.512285</td>\n",
       "      <td>1.405557</td>\n",
       "      <td>0.608297</td>\n",
       "      <td>0.939679</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.361635</td>\n",
       "      <td>1.293015</td>\n",
       "      <td>0.666327</td>\n",
       "      <td>0.962331</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.259820</td>\n",
       "      <td>1.218415</td>\n",
       "      <td>0.705523</td>\n",
       "      <td>0.960041</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.203976</td>\n",
       "      <td>1.117034</td>\n",
       "      <td>0.757190</td>\n",
       "      <td>0.971240</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.134072</td>\n",
       "      <td>1.092641</td>\n",
       "      <td>0.760244</td>\n",
       "      <td>0.973785</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.100221</td>\n",
       "      <td>1.031847</td>\n",
       "      <td>0.792059</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.057083</td>\n",
       "      <td>1.007772</td>\n",
       "      <td>0.803258</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.002322</td>\n",
       "      <td>1.007070</td>\n",
       "      <td>0.801731</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.991323</td>\n",
       "      <td>0.967587</td>\n",
       "      <td>0.820311</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.937207</td>\n",
       "      <td>0.976330</td>\n",
       "      <td>0.814457</td>\n",
       "      <td>0.983965</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.924103</td>\n",
       "      <td>0.957372</td>\n",
       "      <td>0.820311</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.902252</td>\n",
       "      <td>0.924986</td>\n",
       "      <td>0.833036</td>\n",
       "      <td>0.984220</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.873453</td>\n",
       "      <td>0.922718</td>\n",
       "      <td>0.843472</td>\n",
       "      <td>0.983965</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.829751</td>\n",
       "      <td>0.917303</td>\n",
       "      <td>0.840417</td>\n",
       "      <td>0.983456</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.787403</td>\n",
       "      <td>0.887099</td>\n",
       "      <td>0.851107</td>\n",
       "      <td>0.985238</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.724610</td>\n",
       "      <td>0.848128</td>\n",
       "      <td>0.863324</td>\n",
       "      <td>0.983965</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.672168</td>\n",
       "      <td>0.833826</td>\n",
       "      <td>0.872232</td>\n",
       "      <td>0.986765</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.646669</td>\n",
       "      <td>0.830297</td>\n",
       "      <td>0.874777</td>\n",
       "      <td>0.988038</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_metrics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /notebooks/data/imagewoof2\n",
      "Learn path /notebooks/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.896176</td>\n",
       "      <td>1.813364</td>\n",
       "      <td>0.403665</td>\n",
       "      <td>0.882413</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.637129</td>\n",
       "      <td>1.501441</td>\n",
       "      <td>0.562230</td>\n",
       "      <td>0.925681</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.487206</td>\n",
       "      <td>1.374828</td>\n",
       "      <td>0.637567</td>\n",
       "      <td>0.942988</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.325561</td>\n",
       "      <td>1.275634</td>\n",
       "      <td>0.680071</td>\n",
       "      <td>0.965131</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.279334</td>\n",
       "      <td>1.178288</td>\n",
       "      <td>0.730720</td>\n",
       "      <td>0.966404</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.156329</td>\n",
       "      <td>1.130305</td>\n",
       "      <td>0.747264</td>\n",
       "      <td>0.970985</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.125668</td>\n",
       "      <td>1.066135</td>\n",
       "      <td>0.777806</td>\n",
       "      <td>0.974294</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.074676</td>\n",
       "      <td>1.024929</td>\n",
       "      <td>0.793841</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.031411</td>\n",
       "      <td>1.005246</td>\n",
       "      <td>0.809366</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.990445</td>\n",
       "      <td>1.005894</td>\n",
       "      <td>0.797404</td>\n",
       "      <td>0.973021</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.973977</td>\n",
       "      <td>0.968624</td>\n",
       "      <td>0.820565</td>\n",
       "      <td>0.981420</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.941092</td>\n",
       "      <td>0.960810</td>\n",
       "      <td>0.816747</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.906492</td>\n",
       "      <td>0.954217</td>\n",
       "      <td>0.824892</td>\n",
       "      <td>0.983711</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.889860</td>\n",
       "      <td>0.937511</td>\n",
       "      <td>0.831509</td>\n",
       "      <td>0.983456</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.874836</td>\n",
       "      <td>0.913715</td>\n",
       "      <td>0.847798</td>\n",
       "      <td>0.982438</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.820664</td>\n",
       "      <td>0.894813</td>\n",
       "      <td>0.845762</td>\n",
       "      <td>0.985492</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.777499</td>\n",
       "      <td>0.873206</td>\n",
       "      <td>0.855943</td>\n",
       "      <td>0.985238</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.714857</td>\n",
       "      <td>0.836598</td>\n",
       "      <td>0.869432</td>\n",
       "      <td>0.987020</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.661473</td>\n",
       "      <td>0.824193</td>\n",
       "      <td>0.876304</td>\n",
       "      <td>0.987783</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.640547</td>\n",
       "      <td>0.816465</td>\n",
       "      <td>0.877832</td>\n",
       "      <td>0.987783</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IMvQsaZF+Rf/HCxC6O/r3S0t5vLiU0uoDDE4bwGfmjOZTpxUwZfigWJcnIjGikBCKt1TzmfsX0djSxunjc7j1gil8bOowUgfo+Q4i/Z1Cop8rra7ni79eyoisVB76+1kU5qbHuiQR6UV0O2w/VtvYwo2PFNPU2sb915+mgBCRw2hPop9qbXP+8bFlbKio5cEbTmPCUPVXEpHDaU+in/rBi2v565oK7rhkqlpoiEinFBL90JPFpfzyjU18ds5orps7JtbliEgvppDoZ5ZsqeZf//g+Z0zI4dsfP1E3xInIESkk+pGDVzKNGpLGPZ+eyYBE/ecXkSPTp0Q/UdvYwhceLqaltY37ry9icJr6LYlI12IWEmY2z8zWmVmJmd3WwfQfm9ny4LXezPbGos540NrmfO3RZZRU1vKLz5zKeD0pTkS6KSaXwJpZInAP8FGgDFhiZs+4++qD87j718Pm/yow47gXGie+/8JaXllbwXcuO5EzJ+bGuhwR6UNitScxCyhx903u3gQ8Blx2hPnnA48el8rizBPFpdz75iaumzuGa+cWxrocEeljYhUS+UBp2HBZMO4wZjYGGAu82sn0m8ys2MyKKysrI15oX7Z4czXf+uP7nDkhlzsumRrrckSkD4pVSHR03aV3Mu81wO/dvbWjie5+r7sXuXtRXp5uCjuotLqeL/1mKQXBlUxJupJJRI5BrD45yoCCsOFRQHkn816DDjUdlf0NzXz+4SW0tjm/uuE0stIGxLokEemjehwSZvYHM7vYzI5mXUuAiWY21sySCQXBMx2sezIwBHinp3X2F61tzi2PLmNjZR3/+5mZjFXTPhHpgUjsSfwv8Glgg5l9z8ymdLWAu7cANwMvAmuAJ9x9lZndaWaXhs06H3jM3Ts7FCXtfO/5Nby2rpL/uPRETp+gK5lEpGd6fAmsu/8V+KuZZRH6UH/ZzEqB+4DfuHtzJ8s9BzzXbtwd7Yb/X0/r608eX7KN+/62mevnjuGzc9STSUR6LiLnJMwsB7gB+AKwDPgJMBN4ORLrl64t2rSbf3t6JR+ZmMu/60omEYmQHu9JmNlTwBTg18DH3X1HMOlxMyvu6fqla+t37Q9dyZSdxs91JZOIRFAk7rj+ubt3eA+DuxdFYP1yBBt27efT9y1kQGICD1x/GlkDdSWTiEROJL5ynmBmgw8OmNkQM/tKBNYrXSip2M/8+xZhZjx60xw9flREIi4SIXGjux9qvufue4AbI7BeOYKSilquuXcRZvDojXPUtE9EoiISIZFgYU+uCZr3qQ91FJVU1DL/voVAKCD0fGoRiZZInJN4EXjCzP6PUGuNLwEvRGC90oGNlaGAcHceu0kBISLRFYmQ+CbwReDLhHoyvQTcH4H1SjubKmuZf28oIEJ7EJmxLklE4lwkbqZrI3TX9f/2vBzpzOaqOubft5DWNufRm+YwcZgCQkSiLxL3SUwE7gKmAqkHx7v7uJ6uW0I2V9Vxzb3v0NLq/O7GOUxSQIjIcRKJE9cPEtqLaAHOBR4hdGOdRMCWqjrm37uQ5iAgJg9XQIjI8ROJkBjo7q8A5u5bg35L50Vgvf3e1t2hQ0yNLa387sbZCggROe4iceK6IWgTvsHMbga2A0MjsN5+bdvueubfu5CG5lZ+d+McpgwfFOuSRKQfisSexD8CacAtwKnAZ4HrI7Defmvb7nquufcdDjS38tsvzOGEEQoIEYmNHu1JBDfOfdLdbwVqgb+PSFX9WGl1PfPvW0h9cyu//cJspo5UQIhI7PRoTyJ47vSp4Xdcy7Erra7nmnsXUtvYwm8+P5sTR2bFuiQR6ecicU5iGfAnM3sSqDs40t2fisC6+42yPR8ExG+/MJtp+QoIEYm9SIRENrCbD1/R5IBCoptq6puZf99C9jc087sb5yggRKTXiMQd1zoP0UM/fXUDZXsO8Icvn66AEJFeJRJ3XD9IaM/hQ9z9cz1dd3+wpaqOR97ZwqeKCpg5ekisyxER+ZBIHG56Nux9KnAFUB6B9fYL33t+LQMSE/jGxybFuhQRkcNE4nDTH8KHzexR4K89XW9/sGjTbl5YtZN/+ugkhmamdr2AiMhxFomb6dqbCIyOwnrjSlub893n1jAiK5UvfES9EEWkd4rEOYn9fPicxE5Cz5iQI/jTiu28V1bD3Z88hYHJibEuR0SkQ5E43KSuc0fpQFMrP3hhHSflZ3H59PxYlyMi0qkeH24ysyvMLCtseLCZXd7T9cazXy3YxI6aBv7t4hNISNDN6iLSe0XinMS33b3m4IC77wW+HYH1xqWK/Q384vWNXHDiMGaPy4l1OSIiRxSJkOhoHZG4tDYu/fjl9TS3tnHbhSfEuhQRkS5FIiSKzexuMxtvZuPM7MfA0gisN+6s3bmPx5eUcu2cQsbmpse6HBGRLkUiJL4KNAGPA08AB4B/6GohM5tnZuvMrMTMbutknk+a2WozW2Vmv4tArTHj7nz3L2vITB3ALedPiHU5IiLdEomrm+qADj/kOxM8h+Ie4KNAGbDEzJ5x99Vh80wEbgfOcPc9Ztann3b3+vpK/rahijsumcrgtORYlyMi0i2RuLrpZTMbHDY8xMxe7GKxWUCJu29y9ybgMeCydvPcCNzj7nsA3L2ip7XGSktrG//1lzUU5qTx2TljYl2OiEi3ReJwU25wRRMAwYd6V9/684HSsOGyYFy4ScAkM3vLzBaa2byOVmRmN5lZsZkVV1ZWHkP50ffYklI2VNRy+0UnkJwUjZvcRUSiIxKfWG1mdqgNh5kV0kFX2HY6ujmg/TJJhFp8nAPMB+4P32M5tJD7ve5e5O5FeXl5R1H28bG/oZkfv7ye2WOz+djUYbEuR0TkqETiUtVvAQvM7I1g+Czgpi6WKQMKwoZHcXjn2DJgobs3A5vNbB2h0FjS85KPn1+8vpHddU08dPFU9JRXEelrerwn4e4vAEXAOkJXOP0ToSucjmQJMNHMxppZMnAN8Ey7eZ4GzgUws1xCh5829bTe46m0up5fLdjMlTPzOWmUHiYkIn1PJBr8fQH4GqG9geXAHOAdPvw40w9x9xYzuxl4EUgEHnD3VWZ2J1Ds7s8E0z5mZquBVuBWd9/d03qPpx++uI4Eg1svmBzrUkREjkkkDjd9DTiN0KGhc81sCvAfXS3k7s8Bz7Ubd0fYewe+Ebz6nHe37eGZFeXcct4ERmQNjHU5IiLHJBInrhvcvQHAzFLcfS3Qr786uzv/+exq8jJT+OLZ42NdjojIMYvEnkRZcNXR08DLZraHfv740ufe38m72/by/atOIj1FbaxEpO+KxB3XVwRv/5+ZvQZkAS/0dL19VWNLK997YQ1Thmdy9akFXS8gItKLRfRrrru/0fVc8e3ht7dQWn2A33x+Nol6VoSI9HG6/TeCquua+NmrJZw3ZShnTsyNdTkiIj2mkIign/x1PfVNrfzrRVNiXYqISEQoJCKkpKKW3yzaxqdnjWbCUD32W0Tig0IiQu56bg1pAxL5x7+bGOtSREQiRiERAWt37uOVtRV85dwJ5GSkxLocEZGIUUhEwN/WVwFwxYz23c5FRPo2hUQELCipYsLQDIZnpca6FBGRiFJI9FBjSyuLNu/mzAm65FVE4o9Coofe3bqXhuY2zlBIiEgcUkj00FslVSQmGLPHZce6FBGRiFNI9NCCkiqmFwxmUOqAWJciIhJxCokeqKlv5r2yvTrUJCJxSyHRA+9s2k2bo5PWIhK3FBI98FZJFWnJiUwvGBzrUkREokIh0QNvlVQxe2w2yUn6ZxSR+KRPt2O0fe8BNlXVcebEvFiXIiISNQqJY/TWhlArDp2PEJF4ppA4RgtKqsjNSGHSsIxYlyIiEjUKiWPQ1ua8VVLFmRNyMNMjSkUkfikkjsG6XfvZXdek8xEiEvcUEsdgQXA+4owJOTGuREQkuhQSx2BBSRXj89IZkTUw1qWIiESVQuIoNba0snhzta5qEpF+QSFxlJZt28uB5ladjxCRfkEhcZQWbFBrcBHpP2IWEmY2z8zWmVmJmd3WwfQbzKzSzJYHry/Eos72FpRUccqoLLUGF5F+ISYhYWaJwD3AhcBUYL6ZTe1g1sfdfXrwuv+4FtmBmgOh1uA6HyEi/UWs9iRmASXuvsndm4DHgMtiVEu3LTzYGlznI0Skn4hVSOQDpWHDZcG49q4ys/fM7PdmVtDRiszsJjMrNrPiysrKaNR6iFqDi0h/E6uQ6KiXhbcb/jNQ6O4nA38FHu5oRe5+r7sXuXtRXl50v+Ev2KDW4CLSv8Tq064MCN8zGAWUh8/g7rvdvTEYvA849TjV1qGDrcH1qFIR6U9iFRJLgIlmNtbMkoFrgGfCZzCzEWGDlwJrjmN9h3mrJGgNPlEhISL9R1Isfqm7t5jZzcCLQCLwgLuvMrM7gWJ3fwa4xcwuBVqAauCGWNR60FtBa/DJwzJjWYaIyHEVk5AAcPfngOfajbsj7P3twO3Hu66OfNAaPFetwUWkX9EZ2G5Yt2s/VbVNOh8hIv2OQqIbdD5CRPorhUQ3qDW4iPRXCokuNLa0smiTWoOLSP+kkOjCwdbgOh8hIv2RQqILb5WEWoPPGa9HlYpI/6OQ6IJag4tIf6aQOIKaA82sKFVrcBHpvxQSR3CwNbjOR4hIf6WQOIKDrcFnjB4S61JERGJCIXEEC0rUGlxE+jd9+nWifO8BNlWqNbiI9G8KiU4sUCsOERGFRGfUGlxERCHRIfeDrcFz1BpcRPo1hUQH1BpcRCREIdGBBRtC5yMUEiLS3ykkOrCgpIpxeemMHKzW4CLSvykk2mlqaVNrcBGRgEKinWXb9nCguVUhISKCQuIwC0qqSDDUGlxEBIXEYRaUVHFKwWC1BhcRQSHxIfsa1BpcRCScQiLMwo2h1uAKCRGREIVEmAUlVQwcoNbgIiIHKSTCLCipYvY4tQYXETlIn4aBg63BdahJROQDConAW2oNLiJyGIVEINQaPFmtwUVEwsQsJMxsnpmtM7MSM7vtCPNdbWZuZkXRqsXdWVCymzMm5Ko1uIhImJiEhJklAvcAFwJTgflmNrWD+TKBW4BF0awn1Bq8UV1fRUTaidWexCygxN03uXsT8BhwWQfzfQf4AdAQzWJGZA3k7k+ewjmT86L5a0RE+pxYhUQ+UBo2XBaMO8TMZgAF7v7skVZkZjeZWbGZFVdWVh5TMVkDB3DlzFEMzUw9puVFROJVrEKiowP/fmiiWQLwY+CfulqRu9/r7kXuXpSXpz0BEZFIilVIlAEFYcOjgPKw4UxgGvC6mW0B5gDPRPPktYiIHC5WIbEEmGhmY80sGbgGeObgRHevcfdcdy9090JgIXCpuxfHplwRkf4pJiHh7i3AzcCLwBrgCXdfZWZ3mtmlsahJREQOlxSrX+zuzwHPtRt3RyfznnM8ahIRkQ/THdciItIphYSIiHTK3L3rufoIM6sEtsa6jijKBapiXUSUaRvjg7axbxnj7h3eQxBXIRHvzKzY3eP6MmBtY3zQNsYPHW4SEZFOKSRERKRTCom+5d5YF3AcaBvjg7YxTuichIiIdEp7EiIi0imFRIyZ2QNmVmFmK8PGZZvZy2a2Ifg5JBhvZvbT4Gl+75nZzLBlrg/m32Bm18diWzpiZgVm9pqZrTGzVWb2tWB8PG1jqpktNrMVwTb+RzB+rJktCup9POhThpmlBMMlwfTCsHXdHoxfZ2YXxGaLOmdmiWa2zMyeDYbjahvNbIuZvW9my82sOBgXN3+rx8Td9YrhCzgLmAmsDBv3A+C24P1twPeD9xcBzxNqtT4HWBSMzwY2BT+HBO+HxHrbgtpGADOD95nAekJPI4ynbTQgI3g/gNCTFOcATwDXBOP/D/hy8P4rwP8F768BHg/eTwVWACnAWGAjkBjr7Wu3rd8Afgc8GwzH1TYCW4DcduPi5m/1mP5NYl2AXg5Q2C4k1gEjgvcjgHXB+18C89vPB8wHfhk2/kPz9aYX8Cfgo/G6jUAa8C4wm9CNVknB+LnAi8H7F4G5wfukYD4DbgduD1vXofl6w4tQS/9XgPOAZ4Oa420bOwqJuPxb7e5Lh5t6p2HuvgMg+Dk0GN/ZE/26fNJfbxAccphB6Jt2XG1jcBhmOVABvEzoG/JeD9j9UU8AACAASURBVHU8hg/Xe2hbguk1QA69fBuB/wH+BWgLhnOIv2104CUzW2pmNwXj4upv9WjFrAusHJPOnuh3xCf99QZmlgH8AfhHd99n1lHJoVk7GNfrt9HdW4HpZjYY+CNwQkezBT/73Daa2SVAhbsvNbNzDo7uYNY+u42BM9y93MyGAi+b2dojzNtXt/GoaE+id9plZiMAgp8VwfjOnujX1ZP+YsrMBhAKiN+6+1PB6LjaxoPcfS/wOqFj1IPN7OAXsfB6D21LMD0LqKZ3b+MZwKUWelLkY4QOOf0P8bWNuHt58LOCUNjPIk7/VrtLIdE7PQMcvCLiekLH8Q+Ovy64qmIOUBPs/r4IfMzMhgRXXnwsGBdzFtpl+BWwxt3vDpsUT9uYF+xBYGYDgb8j9DCt14Crg9nab+PBbb8aeNVDB6+fAa4JrgwaC0wEFh+frTgyd7/d3Ud56EmR1xCq+TPE0TaaWbqZZR58T+hvbCVx9Ld6TGJ9UqS/v4BHgR1AM6FvIJ8ndOz2FWBD8DM7mNeAewgd734fKApbz+eAkuD197HerrC6ziS0q/0esDx4XRRn23gysCzYxpXAHcH4cYQ+AEuAJ4GUYHxqMFwSTB8Xtq5vBdu+Drgw1tvWyfaewwdXN8XNNgbbsiJ4rQK+FYyPm7/VY3npjmsREemUDjeJiEinFBIiItIphYSIiHRKISEiIp1SSIiISKcUEtLnmFlr0KVzhZm9a2andzH/YDP7SjfW+7qZxf0zi4+GmT1kZld3PafEK4WE9EUH3H26u59CqGHcXV3MP5hQV9JeKeyOZZFeRyEhfd0gYA+E+kOZ2SvB3sX7ZnZZMM/3gPHB3scPg3n/JZhnhZl9L2x9n7DQsyHWm9lHgnkTzeyHZrYkeG7AF4PxI8zszWC9Kw/OHy54PsH3g3UuNrMJwfiHzOxuM3sN+H7wzIKng/UvNLOTw7bpwaDW98zsqmD8x8zsnWBbnwx6Y2Fm3zOz1cG8PwrGfSKob4WZvdnFNpmZ/TxYx1/4oJmd9FP6BiN90UALdVxNJdSa+bxgfANwhYcaCOYCC83sGULPAJjm7tMBzOxC4HJgtrvXm1l22LqT3H2WmV0EfJtQi43PE2q5cJqZpQBvmdlLwJWEWmN/18wSCbUJ78i+YJ3XEep3dEkwfhLwd+7eamY/A5a5++Vmdh7wCDAd+Pfgd58U1D4k2LZ/C5atM7NvAt8ws58DVwBT3N0PtgoB7gAucPftYeM626YZwGTgJGAYsBp4oFv/VSQuKSSkLzoQ9oE/F3jEzKYRapPwX2Z2FqF21vmEPuja+zvgQXevB3D36rBpBxsQLiX0nA8I9d45OezYfBahnkNLgAcs1MDwaXdf3km9j4b9/HHY+Cc91D0WQu1LrgrqedXMcswsK6j1moMLuPseC3VknUrogx0gGXgH2EcoKO8P9gKeDRZ7C3jIzJ4I277Otuks4NGgrnIze7WTbZJ+QiEhfZq7vxN8s84j1BMqDzjV3Zst1LE0tYPFjM5bNzcGP1v54P8PA77q7oc1aQsC6WLg12b2Q3d/pKMyO3lf166mjpbrqFYDXnb3+R3UMws4n1Cw3Ayc5+5fMrPZQZ3LzWx6Z9sU7EGpV48conMS0qeZ2RQgEdhN6NtwRRAQ5wJjgtn2E3p06kEvAZ8zs7RgHeGHmzryIvDlYI8BM5tkoY6hY4Lfdx+hTrczO1n+U2E/3+lknjeBzwTrPweocvd9Qa03h23vEGAhcEbY+Y20oKYMIMvdnwP+kdDhKsxsvLsvcvc7CD0hrqCzbQrquCY4ZzECOLeLfxuJc9qTkL7o4DkJCH0jvj44rv9b4M8WeoD9cmAtgLvvNrO3zGwl8Ly73xp8my42sybgOeBfj/D77id06OldCx3fqSR0TuMc4FYzawZqges6WT7FzBYR+lJ22Lf/wP8DHjSz94B6PmhN/Z/APUHtrcB/uPtTZnYD8GhwPgFC5yj2A38ys9Tg3+XrwbQfmtnEYNwrhLqcvtfJNv2R0Dme9wk9j/yNI/y7SD+gLrAiURQc8ipy96pY1yJyLHS4SUREOqU9CRER6ZT2JEREpFMKCRER6ZRCQkREOqWQEBGRTikkRESkUwoJERHplEJCREQ6FVdtOXJzc72wsDDWZYiI9ClLly6tcve8jqbFVUgUFhZSXFwc6zJERPoUM9va2TQdbhIRkU4pJEREpFMKCRER6ZRCQkREOqWQEBGRTikkRESkU3F1CayISG91oKmV3XWNVNc1sbuuiT11TYfeV9c20dTa1qP1nzhyEF/4yLgIVfsBhYSISA9U7m9k9Y597Nh7gOr60Af+oQ//sNeB5tYOl09KMIakJzNwQGKP6hiY3LPlO6OQEBHphrY2Z1t1Pat37GNVeQ2ry/exqnwfFfsbPzTfwAGJZKcnk5MRek0cmkF2ejLZGcnkpCeTnZ4SGg5eg1KTMLMYbVXXFBIiIu00trSyYVctq8v3HQqFNTv2U9vYAkBigjEhL4MzJ+QydeQgpo4cxOjsNHLSU6L2jT5WFBIi0mfVNbbw+rpKXl69k+r6ZpITE0hJSiA5KYHkxOBn++HEw8cNSEygfO+BIBD2UVKxn+ZWByAtOZETRgziihn5nBgEwqRhmaT28PBQX6GQEJE+ZV9DM6+uqeC593fwxvpKGlvayElPZtSQgTS2tNHc2kZTaxtNLWGv1rZDH/pHkpuRzNSRWZw9Ke9QIBTmpJOY0HsPB0WbQkJEer09dU28vGYXz7+/g7dKdtPU2sawQSlcc1oB86aNYNbY7C4/yNvaPBQeHQRIU0sbeZkpDM1M6dXnB2JBISHSz5VU1PLgW5vZXdt06DDMgM4O24S9T2k3PCQtmRFZqWSnJ0fkg7ZyfyMvrtrJCyt38s6m3bS2OfmDB3Ld3DFceNJwZhQMIeEovuEnJBipCYn95jBRpCgkRPqptTv38fNXS/jL+ztISUpgdHbah75dHzp009JGW9dHag5JTkxgWFYKIwYNZHhWaug1KJURB99npZKXkUJS4uH38u6oOcALK3fy/MqdLNlSjTuMzU3nprPGcdG0EUzLH6Rv+seZQkKkn1m5vYafvrKBl1bvIj05kS+dPZ4vnDmWnIyUTpdpaXeYprHl8OHqukZ21jSwY19D6GdNAyvK9vLCqgaaWj58o1iCwdDMDwJk6KAU3t9ew7JtewGYNCyDW86byIUnDWfysEwFQwwpJET6iXe37eFnr2zgtXWVZKYmccv5E/ncGYUMTkvuctmkxASSEhPoxqyHcXf21Dezs6aBnfsOsKMmFCKh4QZKKmt5a2MVY3LSuPWCycybNpzxeRnHsIUSDVEPCTObB/wESATud/fvtZs+BngAyAOqgc+6e1kw7QfAxYR6TL0MfM3dj2LHV6T321PXxHeeXQ3ArLHZzB6XQ2FOWsS+PS/atJufvVrCgpIqhqQN4NYLJnPt3DEMSh0QkfV3xcwO3Tg2deSg4/I7JXKiGhJmlgjcA3wUKAOWmNkz7r46bLYfAY+4+8Nmdh5wF3CtmZ0OnAGcHMy3ADgbeD2aNYscT2V76rn+gcWU7jlAZkoSTy3bDsDQzJRQYAShMSEv46hO0ro7C0qq+NkrJSzeUk1uRgr/etEUPjN7DOkpOoAg3Rftv5ZZQIm7bwIws8eAy4DwkJgKfD14/xrwdPDegVQgGTBgALAryvWKHDery/dxw4OLaWhu5Tefn81phUPYWFnHos27Wby5mkWbqnn2vR0ADEkbwKyx2cwam8PssdmcMGJQh5d8ujuvravgp6+UsLx0L8MHpfLtj09l/qzRuqpHjkm0QyIfKA0bLgNmt5tnBXAVoUNSVwCZZpbj7u+Y2WvADkIh8XN3X9P+F5jZTcBNAKNHj478FohEwdslVXzx10vJSE3i918+nUnDMgGYMDSDCUMz+MzsMbg7pdUHWLR5N4s2V7N4czUvrgp9T8pMTeK0wuwgOLKZNjKLV9dW8PPXNrBy+z7yBw/ku1dM4+pTR5GSpHCQYxftkOho/7j9OYV/Bn5uZjcAbwLbgRYzmwCcAIwK5nvZzM5y9zc/tDL3e4F7AYqKinS+Qnq9Z1aU809PLGdcbgYPfe40RmQN7HA+M2N0Thqjc9L4RFEBAOV7D7BkSzULN1WzePNuXl1bAYR6CbW2OYU5afzg6pO5YkY+Azq4xFTkaEU7JMqAgrDhUUB5+AzuXg5cCWBmGcBV7l4T7CEsdPfaYNrzwBxCQSLSJ93/t03851/WMGtsNvddV0TWwKM7eTxy8EAum57PZdPzgdANZ0u2VLNs2x6m5Wdx8UkjOrz/QORYRTsklgATzWwsoT2Ea4BPh89gZrlAtbu3AbcTutIJYBtwo5ndRWiP5Gzgf6Jcr0hUtLU5//XcGu5fsJkLpw3nx5+aHpFzBHmZKVx00gguOmlEBKoUOVxUQ8LdW8zsZuBFQpfAPuDuq8zsTqDY3Z8BzgHuMjMntJfwD8HivwfOA94ndIjqBXf/czTrlf6jrc3ZVFXLu9v2srx0L6vL9zFj9GA+f+ZYRg1Ji+jvamxp5dYn3+OZFeVcP3cMd3z8xH7dME76Foun2w6Kioq8uLg41mVIL1Rd18Ty0j0sC0Jheele9jeEng2QmZrEpGGZrCjdiwMfP3kEN501PiLX9O9vaOaLv17K2xt38815U/jS2eN097D0Oma21N2LOpqmC6Yl7jS1tLF6xz6Wb9vDsiAQtu6uB0IneCcPy+TSU0YyvWAwM0YPYVxuOgkJRvneAzywYDOPLt7G08vL+cjEXL509nhOH59zTB/sFfsauP7BJWzYtZ+7P3kKV84c1fVCIr2M9iSkz2pobmVX0CeovOYA75ftY1npHlaV7zvUK2jYoBRmFAxhxujBTC8YzEmjskhLPvJ3o5r6Zn6zaCsPvrWFqtpGpuUP4otnjefCacO7fVJ4Y2Ut1/1qMXvqm/jfz57K2ZPyery9ItFypD0JhYT0Svsbmtm1L9QkbkdNA7vaNY7bta+B6rqmDy2TOiCBk/MHM330YGYUhH52dnlpdzQ0t/LHZdu5781NbKqqoyB7IDd+ZByfOLXgiI+oXLp1D59/eAlJCcaDN8zipFFZx1yDyPGgkJBeq7XNWbhpN8+v3MHW3fWHmr8dfJZwuOz05EMtp4dlpTJiUKiL6IisgQzPSmFMTnpU7g1obXNeXr2LX765kWXb9pKdnsx1c8dw3dxCstM/3PHu5dW7+Oqj7zJ8UCoPf24WY3LSI16PSKQpJKRXcXfe317Dn5aX8+cV5VTsbyQ9OZEJwzIPffCHPvwPPodgIEMHpcS8rYS7U7x1D798YyN/XVNB6oAEPlVUwBc+Mo6C7DQeXbyNb/3xfU7Kz+JXN5xG7hFab4v0JjpxLb3Cpspa/rS8nGdWlLO5qo7kxATOmZzHZdPzOf+EoTEPga6YGacVZnNaYTYbdu3n3jc38bvF2/j1wq0UFWazeHM1507O457PzOzyvIdIX6E9CYmqXfsa+POKUDC8V1aDGcwdl8Nl00cy78QRZKUdn3bV0bKzpoEH39rM7xZt4+KTR/Cdy6epHYb0OTrcJMdVzYFmXli5gz8tL+edTbtxh5Pys7hs+kguOXkkw7NSY11ixLW1+VG18hbpTXS4SaKuobmVV9dW8Kfl23ltbSVNrW0U5qRxy3kTuXT6yLh/0pgCQuKVQkJ6ZG99Ew8s2MxDb29hX0MLeZkpfHbOGC6fMZKT8rN0d7FIH6eQkGNSVdvI/X/bzK/f2UJdUysXnDiM6+YWMmdcjvoSicQRhYQclV37GvjlG5v43eKtNLa0ccnJI7n53AlMHp4Z69JEJAoUEtIt2/ce4P9e38jjxaW0tjmXT8/nK+eOj/tzDSL9nUJCjmjb7np+8XoJf3i3DICrTx3Fl8+ewOicyLbTFpHeSSEhHdpYWcs9r5Xwp+XlJCYY82eN5otnjyd/8LH3QhKRvkchIR+ybud+fvbqBv7y/g5SkhK44fRCbjprHMMGxd+9DSLSNYWE0NDcyqryfdz75kZeXLWL9OREvnT2eD5/5lj1HxLp5xQS/YC7U13XxNbqekqr69m6O/Qqra5na3Udu/Y1AqEntN1y/kQ+d0Yhg9OSu1iriPQHCok40dLaRvneBrZW130QALvrDwVD+9bbwwelMjo7jY9MzGNMdhqjc9I4d8pQBqX27V5KIhJZCok4sGtfA5f+fMGhPQKA5KQECoYMZExOOrPHZjM6O40xOWmMzk6jIDut13dcFZHeQSERB367cCsV+xv5zuXTmDg0gzE5aQzLTFU/IRHpMYVEH9fY0srvFm/j3MlDuXbOmFiXIyJxRo3v+7jn399JVW0T159eGOtSRCQOKST6uIfe3sK43HQ+MiE31qWISBxSSPRhK0r3srx0L9fOHaPzDyISFVEPCTObZ2brzKzEzG7rYPoYM3vFzN4zs9fNbFTYtNFm9pKZrTGz1WZWGO16+5KH39lCenIiV586qst5RUSORVRDwswSgXuAC4GpwHwzm9puth8Bj7j7ycCdwF1h0x4BfujuJwCzgIpo1tuX7K5t5NkVO7hy5igydW+DiERJtPckZgEl7r7J3ZuAx4DL2s0zFXgleP/awelBmCS5+8sA7l7r7vVRrrfPeGxJKU2tbVx/uq5oEpHoiXZI5AOlYcNlwbhwK4CrgvdXAJlmlgNMAvaa2VNmtszMfhjsmXyImd1kZsVmVlxZWRmFTeh9Wlrb+M3CrZw5IZcJQ/WwHxGJnmiHREdnU73d8D8DZ5vZMuBsYDvQQugejo8E008DxgE3HLYy93vdvcjdi/Ly8iJYeu/18upd7Khp4Lq52osQkeiKdkiUAQVhw6OA8vAZ3L3c3a909xnAt4JxNcGyy4JDVS3A08DMKNfbJzz09hbyBw/k/BOGxboUEYlz0Q6JJcBEMxtrZsnANcAz4TOYWa6ZHazjduCBsGWHmNnB3YPzgNVRrrfXW7tzH4s2V3Pt3DEk6rJXEYmyqIZEsAdwM/AisAZ4wt1XmdmdZnZpMNs5wDozWw8MA74bLNtK6FDTK2b2PqFDV/dFs96+4OG3t5KSlMCnigq6nllEpIei3rvJ3Z8Dnms37o6w978Hft/Jsi8DJ0e1wD6kpr6Zp5dt57LpIxmSruc9iEj06Y7rPuTJpaUcaG5VnyYROW66FRJm9iMzOzHaxUjnWtucR97ZymmFQzhxZFasyxGRfqK7exJrgXvNbJGZfcnM9Cl1nL2xvoJt1fVcN7cw1qWISD/SrZBw9/vd/QzgOqAQeM/Mfmdm50azOPnAQ29vZWhmCvOmDY91KSLSj3T7nERwt/OU4FVF6E7pb5jZY1GqTQKbKmt5c30ln5k9hgGJOo0kIsdPt65uMrO7gUsJ9Vj6L3dfHEz6vpmti1ZxEvLIO1sZkGjMn63LXkXk+OruJbArgX/rpMHerAjWI+3UNrbw+6VlXHzSCIZmpsa6HBHpZ7p77GIPcKgftZkNNrPL4VALDYmSP75bRm1jC9fpslcRiYHuhsS3w8PA3fcC345OSXKQu/PwO1s5eVQWMwoGx7ocEemHuhsSHc0X9bu1+7u3N+6mpKKW6+YWYqY+TSJy/HU3JIrN7G4zG29m48zsx8DSaBYmoW6v2enJXHLyiFiXIiL9VHdD4qtAE/A48CTQAPxDtIoSKK2u55U1u5g/q4DUAYc9a0lE5Ljo1iEjd68DbotyLRLmN4u2YmZ8ZrYeLCQisdPd+yTygH8BTgQOXYfp7udFqa5+raG5lceXlPKxqcMYOXhgrMsRkX6su4ebfkuof9NY4D+ALYQeCiRR8MzycvbWN6tPk4jEXHdDIsfdfwU0u/sb7v45YE4U6+q33J2H3t7C5GGZzBmXHetyRKSf625INAc/d5jZxWY2g9DzqiXClm7dw+od+7ju9DG67FVEYq679zr8Z9Ae/J+AnwGDgK9Hrap+7KG3tzAoNYkrZuTHuhQRka5DIuj+OtHdnwVqALUHj5Jd+xp4YeVObji9kLRk3asoIrHX5eEmd28l1AFWouy3i7bR6s61c3XZq4j0Dt39uvq2mf2c0M10dQdHuvu7UamqH2pqaeN3i7Zx7uShjMlJj3U5IiJA90Pi9ODnnWHjHNB9EhHy/ModVNU2cp32IkSkF+nuHdc6DxFlD729hbG56Zw1MS/WpYiIHNLdO67v6Gi8u9/Z0Xg5Oos27WbZtr18++NTSUjQZa8i0nt093BTXdj7VOASYE3ky+l/3J3vvbCW4YNSuea00bEuR0TkQ7p1M527/3fY67vAOUC3LuQ3s3lmts7MSszssCaBZjbGzF4xs/fM7HUzG9Vu+iAz2x6cOI87L6zcybJte/n6RycyMFndXkWkd+nuHdftpQHjupopuMfiHuBCYCow38ymtpvtR8Aj7n4yoRPjd7Wb/h3gjWOss1drbm3jBy+uY+LQDK6aqRvYRaT36VZImNn7wTf998xsFbAO+Ek3Fp0FlLj7JndvAh4DLms3z1TgleD9a+HTzexUYBjwUnfq7GseW1LK5qo6vjlvCkmJx5rXIiLR091zEpeEvW8Bdrl7SzeWywdKw4bLgNnt5lkBXEUodK4AMs0sB9gD/DdwLXB+N+vsM+oaW/jJXzcwqzCb808YGutyREQ61N2vryOAanff6u7bgVQza/9h35GOLtXxdsP/DJxtZsuAs4HthILoK8Bz7l7KEZjZTWZWbGbFlZWV3Sipd7jvb5uoqm3ktoumqJGfiPRa3d2T+F9gZthwfQfjOlIGFIQNjwLKw2dw93LgSgAzywCucvcaM5sLfMTMvgJkAMlmVuvut7Vb/l7gXoCioqL2AdQrVe5v5N43N3HhtOHMHD0k1uWIiHSquyFh7n7oA9jd28ysO8suASaa2VhCewjXAJ/+0IrNcgntpbQBtwMPBL/jM2Hz3AAUtQ+Ivuqnr2ygsaWNWy+YHOtSRESOqLuHmzaZ2S1mNiB4fQ3Y1NVCwXmLm4EXCd1X8YS7rzKzO83sYNPAc4B1Zrae0Enq7x71VvQhm6vqeHTxNubPKmBcXkasyxEROSIL20HofCazocBPCfVqckJXI/2ju1dEt7yjU1RU5MXFxbEu44i+8tulvL6uktdvPYehmaldLyAiEmVmttTdizqa1t3eTRWEDhVJDyzbtofn3t/JLedPVECISJ/Q3fskHjazwWHDQ8zsgeiVFX/cnbueX0tuRjI3ndXlfYgiIr1Cd89JnOzuew8OuPseYEZ0SopPr66tYPHmam45fyIZKXrqnIj0Dd0NiQQzO3Stppll0/0ro/q91jbn+y+spTAnjfmz1MRPRPqO7n7Q/zehp9P9Phj+BHF+FVIk/WFpGet31XLPp2cyQO03RKQP6e6J60fMbClwLqG7qK9099VRrSxONDS3cvfL6zmlYDAXnTQ81uWIiByVbh8yCu5vqCT0PAnMbLS7b4taZXHiwbe2sHNfA/9zzXS13xCRPqe7VzddamYbgM2E2nZvAZ6PYl1xYU9dE794vYTzpgxlzricWJcjInLUunuA/DvAHGC9u48l1JX1rahVFSfuea2EusYWvjlvSqxLERE5Jt0NiWZ3303oKqcEd38NmB7Fuvq80up6HnlnK1fNHMXk4ZmxLkdE5Jh095zE3qBD65vAb82sglA7b+nE3S+vxwy+8bFJsS5FROSYdXdP4jJC7cG/DrwAbAQ+Hq2i+rpV5TU8vXw7f3/GWEZkDYx1OSIix6y7l8DWBW/bgIfbTzezd9x9biQL68u+9/xasgYO4MvnjI91KSIiPRKpO7vUrS6wYEMVf9tQxc3nTiBr4IBYlyMi0iORCok+8US4aGtrc+56fg35gwdy7dwxsS5HRKTH1CMigv78XjmryvfxzxdMIiUpMdbliIj0WKRCot/fStzY0soPX1zHCSMGcdkp+bEuR0QkIrp7x/WFHYz7UtjgtRGrqI/6zcJtlO05wG0XTiEhod9npojEie7uSfy7mZ13cMDMvknoslgA3H1lpAvrS+oaW/j5qxs4Y0IOZ03MjXU5IiIR092b6S4FnjWzW4F5wJRgnABLtlSzp76ZL541Xk38RCSudPc+iSozuxT4K7AUuNrddUVTYNm2vZjBzDFDup5ZRKQPOWJImNl+Pnx5azIwDrjazNzdB0WzuL5ieeleJg3N1GNJRSTuHPFTzd271ZnOzE5091WRKalvcXeWl+7lwml6oJCIxJ9IXQL76witp8/ZXFVHzYFmphcMjnUpIiIRp/skemjZtr0AzBit8xEiEn+i3pbDzOaZ2TozKzGz2zqYPsbMXjGz98zsdTMbFYyfbmbvmNmqYNqnIlRrRC0v3Ut6ciIThmbEuhQRkYiLalsOM0sE7gEuBKYC881sarvZfgQ84u4nA3cCdwXj64Hr3P1EQpfd/o+Z9bpjOstK93BKwWASdQOdiMShSIVEUyfjZwEl7r7J3ZuAxwi7CS8wFXgleP/awenuvt7dNwTvy4EKIC9C9UbEgaZW1uzYz4zRvS67REQiotshYWZXmtndZvbfZnZF+DR3n9PJYvlAadhwWTAu3ArgquD9FUCmmeW0+92zCF1+u7G79R4PK8traG1zphfofISIxKfu9m76BfAl4H3g/7d370FyVNcdx78/Vm/0RCwqgWQQBhsEyAILMCYBWbYxEBc2LyPiFGBTwbELVxw/USC8EhIIlJNKTMoQBzB+YAMBW8EiguJhqiheglkJgSwQxN4RItaCdlYCJEtaTv7oO9CMZtBIYXZ2Z36fqqnpuXO751yxzJnu2316OfAlSdfWs2qVtsr5i28Cx0oqAMcCL5G7NaqkqWRnT30hIt6sEtt5kpZIWtLT01PPcN4zhe5eAJ/ZZGYtq96rv44FDi5fZS3ph2QJY3tWA9Nzr6cBa/Id0qGkU9J2xwKnRkRfej0e+BVwUUQ8Wu0DIuJ64HqAOXPmDOhV4F3FEtMmjaZzqTfgbQAAD8pJREFU3MiB/FgzswFT7+GmlcD7cq+nA8vqWO8JYH9JMySNAOYDC/MdJO0uqRzHAuCG1D4CuJNsUvu2OuMcUIXukk99NbOWVm+SmAysSKeoPgg8C3RKWihpYa2VImIrcD6wGFgB3BoRz0i6PNWCApgLrJT0HDAFuCK1fw44BjhHUld6zN7B8TXM//Zt4uW+TRzqQ01m1sLqPdx08c5+QEQsAhZVtF2cW74duL3Kej8Gfryzn9toXcU0H+Ezm8yshdVbBfbXkqYAh6emxyNibePCGvwK3SVGdOzCQXu6xqGZta56z276HPA4cDrZYaDHJJ3WyMAGu0KxxIF7jve9rM2spdV7uOlC4PDy3oOkTrJ7S2xzmKgdbO1/k6dX93HG4dO339nMbAird+J6l4rDS6/uwLotZ+XvN7BxS7+vtDazllfvnsTdkhYDt6TXZ1AxGd1O3qr86iutzazF1bs3EMB1wCzgQ6SL19pVV7HE5F1HMH230c0Oxcysoerdk/hkRHwHuKPcIOky4DsNiWqQK3T3Mnv6RCRXfjWz1vauexKSvizpaeCD6Z4O5cf/UN8V1y2nb+MWXuh53fMRZtYWtrcn8VPgbrJ7PORvGLQhItY1LKpBbGkxm49w5VczawfvmiRSob0+4MyBCWfwK3SXkGDW9AnNDsXMrOHa9jTWndVV7GW/zrGMHzW82aGYmTWck8QOiAi6iiXPR5hZ23CS2AG/e/UNet/Y4vLgZtY2nCR2QKHoO9GZWXtxktgBXd0lxozo4ANTxjU7FDOzAeEksQMKxRKzpk2gYxdfRGdm7cFJok6btvTz7Jr1no8ws7biJFGnZ9b0sfXN8O1KzaytOEnUqVz51bcrNbN24iRRp0KxxF4TR7PHuFHNDsXMbMA4SdSpq7vkvQgzaztOEnVYu34TL5U2ej7CzNqOk0QdCqnyq8txmFm7cZKoQ6G7xPAOcdCervxqZu3FSaIOXcVeZk4dz6jhHc0OxcxsQDlJbEf/m8Gy1X2u12RmbanhSULS8ZJWSlol6YIq7+8t6b50W9QHJU3LvXe2pOfT4+xGx1rNc7/fwBub+32ltZm1pYYmCUkdwLXACcBM4ExJMyu6XQPcHBGzgMvJbpWKpN2AS4AjgSOASyQN+Df1WxfReU/CzNpQo/ckjgBWRcSLEbEZ+BnwmYo+M4H70vIDufc/BdwbEesiohe4Fzi+wfFuo6vYy6Qxw9l78piB/mgzs6ZrdJLYCyjmXq9ObXlLgVPT8snAOEmT61wXSedJWiJpSU9Pz3sWeFmhu8Ts6RORXPnVzNpPo5NEtW/WqHj9TeBYSQXgWOAlYGud6xIR10fEnIiY09nZ+f+N9x3Wb9rCqp7XPB9hZm1rWIO3vxqYnns9DViT7xARa4BTACSNBU6NiD5Jq4G5Fes+2MhgKy0r9hHhi+jMrH01ek/iCWB/STMkjQDmAwvzHSTtLqkcxwLghrS8GDhO0qQ0YX1cahswhe7sdqWzpjlJmFl7amiSiIitwPlkX+4rgFsj4hlJl0s6KXWbC6yU9BwwBbgirbsO+FuyRPMEcHlqGzBdxRL77TGWCaOHD+THmpkNGo0+3ERELAIWVbRdnFu+Hbi9xro38PaexYCKCArFEvMO2KMZH29mNij4iusaius2su71zZ6PMLO25iRRQ6GYzUf4Ijoza2dOEjUUukuMHt7BB6eMa3YoZmZN4yRRQ6FYYta0CQzr8D+RmbUvfwNWsWlLP8+u6fPtSs2s7TlJVPHsy+vZ0h8cOt1XWptZe3OSqKJc+dVnNplZu3OSqKKrWGLPCaOYMn5Us0MxM2sqJ4kqCt29LupnZoaTxDZ6NvyB1b0bfX2EmRlOEtvoKno+wsyszEmiQlexl2G7iIP3mtDsUMzMms5JokKhu8SBU8czanhHs0MxM2s6J4mc/jeDpcWS5yPMzBIniZxVa1/j9c39no8wM0ucJHLKd6Lz6a9mZhkniZyuYomJY4azz+QxzQ7FzGxQcJLIKXRn8xGSmh2Kmdmg4CSRvPaHrTy3doMnrc3McpwkkmXFEhGejzAzy3OSSArpSuvZ07wnYWZW5iSRFLpL7Nu5KxPGDG92KGZmg4aTBBARdBV7fZMhM7MKThLA6t6NvPLaZt+u1MysgpMEIME5H92Ho/ad3OxQzMwGlYYnCUnHS1opaZWkC6q8/z5JD0gqSFom6cTUPlzSDyU9LWmFpAWNinHapDFcetJB7LfH2EZ9hJnZkNTQJCGpA7gWOAGYCZwpaWZFt4uAWyPiUGA+8G+p/XRgZEQcAnwY+JKkfRoZr5mZvVOj9ySOAFZFxIsRsRn4GfCZij4BjE/LE4A1ufZdJQ0DRgObgfUNjtfMzHIanST2Aoq516tTW96lwJ9JWg0sAr6a2m8HXgdeBrqBayJiXeUHSDpP0hJJS3p6et7j8M3M2lujk0S1IkhR8fpM4KaImAacCPxI0i5keyH9wJ7ADOAbkvbdZmMR10fEnIiY09nZ+d5Gb2bW5hqdJFYD03Ovp/H24aSyc4FbASLiEWAUsDvwp8B/R8SWiFgLPAzMaXC8ZmaW0+gk8QSwv6QZkkaQTUwvrOjTDXwcQNKBZEmiJ7XPU2ZX4CPAbxocr5mZ5TQ0SUTEVuB8YDGwguwspmckXS7ppNTtG8CfS1oK3AKcExFBdlbUWGA5WbK5MSKWNTJeMzN7J2Xfx61BUg/wu2bH0UC7A680O4gG8xhbg8c4tOwdEVUndVsqSbQ6SUsioqXnZTzG1uAxtg6X5TAzs5qcJMzMrCYniaHl+mYHMAA8xtbgMbYIz0mYmVlN3pMwM7OanCSaTNINktZKWp5r203SvZKeT8+TUrsk/Usqu75M0mG5dc5O/Z+XdHYzxlKNpOmpFPwKSc9I+svU3kpjHCXpcUlL0xgvS+0zJD2W4v15uqAUSSPT61Xp/X1y21qQ2ldK+lRzRlSbpI5U1v+u9Lqlxijpt+n2BF2SlqS2lvlb3SkR4UcTH8AxwGHA8lzbPwIXpOULgKvS8onA3WQ1sT4CPJbadwNeTM+T0vKkZo8txTYVOCwtjwOeIysb30pjFDA2LQ8HHkux3wrMT+3fB76clr8CfD8tzwd+npZnAkuBkWT1yl4AOpo9voqxfh34KXBXet1SYwR+C+xe0dYyf6s79W/S7AD8CIB9KpLESmBqWp4KrEzL1wFnVvYjK5J4Xa79Hf0G0wP4JfDJVh0jMAZ4CjiS7EKrYan9KGBxWl4MHJWWh6V+AhYAC3LbeqvfYHiQ1V67D5gH3JVibrUxVksSLfm3Wu/Dh5sGpykR8TJAet4jtdcqvV5PSfamS4ccDiX7pd1SY0yHYbqAtcC9ZL+QS5GVpoF3xvvWWNL7fcBkBvkYgX8Gvg28mV5PpvXGGMA9kp6UdF5qa6m/1R01rNkB2A6pVXq9npLsTSVpLPCfwNciYr1ULeSsa5W2QT/GiOgHZkuaCNwJHFitW3oecmOU9GlgbUQ8KWluublK1yE7xuToiFgjaQ/gXknvVlR0qI5xh3hPYnD6vaSpAOl5bWqvVXq9npLsTSNpOFmC+ElE3JGaW2qMZRFRAh4kO0Y9UdmdFeGd8b41lvT+BGAdg3uMRwMnSfot2R0m55HtWbTSGImINel5LVmyP4IW/Vutl5PE4LQQKJ8RcTbZcfxy+1nprIqPAH1p93cxcJykSenMi+NSW9Mp22X4D2BFRHw391YrjbEz7UEgaTTwCbKqxw8Ap6VulWMsj/004P7IDl4vBOanM4NmAPsDjw/MKN5dRCyIiGkRsQ/ZRPT9EfF5WmiMknaVNK68TPY3tpwW+lvdKc2eFGn3B1l59JeBLWS/QM4lO3Z7H/B8et4t9RVZCfUXgKeBObntfBFYlR5faPa4cnH9Edmu9jKgKz1ObLExzgIKaYzLgYtT+75kX4CrgNuAkal9VHq9Kr2/b25bF6axrwROaPbYaox3Lm+f3dQyY0xjWZoezwAXpvaW+VvdmYevuDYzs5p8uMnMzGpykjAzs5qcJMzMrCYnCTMzq8lJwszManKSsCFHUn+q0rlU0lOSPrqd/hMlfaWO7T4oqeXvWbwjJN0k6bTt97RW5SRhQ9HGiJgdER8iKxj3D9vpP5GsKumglLti2WzQcZKwoW480AtZfShJ96W9i6clfSb1uRJ4f9r7uDr1/Xbqs1TSlbntna7s3hDPSfrj1LdD0tWSnkj3DfhSap8q6aG03eXl/nnp/gRXpW0+Lmm/1H6TpO9KegC4Kt2z4Bdp+49KmpUb040p1mWSTk3tx0l6JI31tlQbC0lXSno29b0mtZ2e4lsq6aHtjEmSvpe28SveLmZnbcq/YGwoGq2s4uoostLM81L7JuDkyAoI7g48Kmkh2T0ADo6I2QCSTgA+CxwZEW9I2i237WERcYSkE4FLyEpsnEtWcuFwSSOBhyXdA5xCVhr7CkkdZGXCq1mftnkWWb2jT6f2DwCfiIh+Sf8KFCLis5LmATcDs4G/SZ99SIp9UhrbRWnd1yV9B/i6pO8BJwMHRESUS4UAFwOfioiXcm21xnQo8EHgEGAK8CxwQ13/VawlOUnYULQx94V/FHCzpIPJyiT8vaRjyMpZ70X2RVfpE8CNEfEGQESsy71XLkD4JNl9PiCrvTMrd2x+AlnNoSeAG5QVMPxFRHTViPeW3PM/5dpvi6x6LGTlS05N8dwvabKkCSnW+eUVIqJXWUXWmWRf7AAjgEeA9WSJ8gdpL+CutNrDwE2Sbs2Nr9aYjgFuSXGtkXR/jTFZm3CSsCEtIh5Jv6w7yWpCdQIfjogtyiqWjqqymqhduvkP6bmft///EPDViNimSFtKSH8C/EjS1RFxc7Uwayy/XhFTtfWqxSrg3og4s0o8RwAfJ0ss5wPzIuIvJB2Z4uySNLvWmNIelGv12Fs8J2FDmqQDgA7gVbJfw2tTgvgYsHfqtoHs1qll9wBflDQmbSN/uKmaxcCX0x4Dkj6grGLo3unz/p2s0u1hNdY/I/f8SI0+DwGfT9ufC7wSEetTrOfnxjsJeBQ4Oje/MSbFNBaYEBGLgK+RHa5C0vsj4rGIuJjsDnHTa40pxTE/zVlMBT62nX8ba3Hek7ChqDwnAdkv4rPTcf2fAP+l7Ab2XcBvACLiVUkPS1oO3B0R30q/ppdI2gwsAv76XT7vB2SHnp5Sdnynh2xOYy7wLUlbgNeAs2qsP1LSY2Q/yrb59Z9cCtwoaRnwBm+Xpv474NoUez9wWUTcIekc4JY0nwDZHMUG4JeSRqV/l79K710taf/Udh9ZldNlNcZ0J9kcz9Nk9yP/9bv8u1gbcBVYswZKh7zmRMQrzY7FbGf4cJOZmdXkPQkzM6vJexJmZlaTk4SZmdXkJGFmZjU5SZiZWU1OEmZmVpOThJmZ1fR/PdEFH3X0CMsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_metrics()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /notebooks/data/imagewoof2\n",
      "Learn path /notebooks/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.879772</td>\n",
       "      <td>1.862322</td>\n",
       "      <td>0.410537</td>\n",
       "      <td>0.866887</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.644690</td>\n",
       "      <td>1.669008</td>\n",
       "      <td>0.517180</td>\n",
       "      <td>0.923136</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.456890</td>\n",
       "      <td>1.331606</td>\n",
       "      <td>0.652838</td>\n",
       "      <td>0.955968</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.331498</td>\n",
       "      <td>1.240978</td>\n",
       "      <td>0.698906</td>\n",
       "      <td>0.963095</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.251138</td>\n",
       "      <td>1.175720</td>\n",
       "      <td>0.722576</td>\n",
       "      <td>0.963349</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.168022</td>\n",
       "      <td>1.113267</td>\n",
       "      <td>0.756681</td>\n",
       "      <td>0.970221</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.128489</td>\n",
       "      <td>1.092905</td>\n",
       "      <td>0.764062</td>\n",
       "      <td>0.974294</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.071325</td>\n",
       "      <td>1.053600</td>\n",
       "      <td>0.784678</td>\n",
       "      <td>0.976330</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.038296</td>\n",
       "      <td>0.981178</td>\n",
       "      <td>0.813184</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.007002</td>\n",
       "      <td>0.998843</td>\n",
       "      <td>0.801222</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.958826</td>\n",
       "      <td>0.985198</td>\n",
       "      <td>0.807839</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.958943</td>\n",
       "      <td>0.958742</td>\n",
       "      <td>0.826673</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.926303</td>\n",
       "      <td>0.960646</td>\n",
       "      <td>0.827182</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.913483</td>\n",
       "      <td>0.935954</td>\n",
       "      <td>0.834563</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.881385</td>\n",
       "      <td>0.909846</td>\n",
       "      <td>0.846526</td>\n",
       "      <td>0.984474</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.823736</td>\n",
       "      <td>0.900625</td>\n",
       "      <td>0.846780</td>\n",
       "      <td>0.982438</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.779758</td>\n",
       "      <td>0.886093</td>\n",
       "      <td>0.853652</td>\n",
       "      <td>0.983202</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.720723</td>\n",
       "      <td>0.860730</td>\n",
       "      <td>0.859252</td>\n",
       "      <td>0.987020</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.672059</td>\n",
       "      <td>0.832210</td>\n",
       "      <td>0.873250</td>\n",
       "      <td>0.986002</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.644315</td>\n",
       "      <td>0.825873</td>\n",
       "      <td>0.878595</td>\n",
       "      <td>0.986765</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_metrics()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /notebooks/data/imagewoof2\n",
      "Learn path /notebooks/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.866132</td>\n",
       "      <td>1.743006</td>\n",
       "      <td>0.431662</td>\n",
       "      <td>0.885213</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.649543</td>\n",
       "      <td>1.669000</td>\n",
       "      <td>0.515907</td>\n",
       "      <td>0.923390</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.487756</td>\n",
       "      <td>1.390319</td>\n",
       "      <td>0.618733</td>\n",
       "      <td>0.945024</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.333780</td>\n",
       "      <td>1.234973</td>\n",
       "      <td>0.705523</td>\n",
       "      <td>0.962840</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.263016</td>\n",
       "      <td>1.185292</td>\n",
       "      <td>0.721303</td>\n",
       "      <td>0.963095</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.180126</td>\n",
       "      <td>1.108759</td>\n",
       "      <td>0.762026</td>\n",
       "      <td>0.973021</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.127670</td>\n",
       "      <td>1.084044</td>\n",
       "      <td>0.769661</td>\n",
       "      <td>0.969203</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.057895</td>\n",
       "      <td>1.055403</td>\n",
       "      <td>0.777043</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.027599</td>\n",
       "      <td>1.033200</td>\n",
       "      <td>0.791041</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.998072</td>\n",
       "      <td>1.009715</td>\n",
       "      <td>0.799186</td>\n",
       "      <td>0.975821</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.989259</td>\n",
       "      <td>0.959206</td>\n",
       "      <td>0.816238</td>\n",
       "      <td>0.981420</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.944197</td>\n",
       "      <td>0.963302</td>\n",
       "      <td>0.824892</td>\n",
       "      <td>0.982184</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.923886</td>\n",
       "      <td>0.941441</td>\n",
       "      <td>0.829982</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.894658</td>\n",
       "      <td>0.943316</td>\n",
       "      <td>0.837109</td>\n",
       "      <td>0.982947</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.864580</td>\n",
       "      <td>0.924537</td>\n",
       "      <td>0.834054</td>\n",
       "      <td>0.983202</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>0.906306</td>\n",
       "      <td>0.847035</td>\n",
       "      <td>0.982184</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.764547</td>\n",
       "      <td>0.879034</td>\n",
       "      <td>0.852634</td>\n",
       "      <td>0.985238</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.716479</td>\n",
       "      <td>0.842884</td>\n",
       "      <td>0.870196</td>\n",
       "      <td>0.987020</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.663724</td>\n",
       "      <td>0.825060</td>\n",
       "      <td>0.880122</td>\n",
       "      <td>0.985238</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.641334</td>\n",
       "      <td>0.827884</td>\n",
       "      <td>0.880377</td>\n",
       "      <td>0.984474</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /notebooks/data/imagewoof2\n",
      "Learn path /notebooks/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.930055</td>\n",
       "      <td>1.766917</td>\n",
       "      <td>0.418682</td>\n",
       "      <td>0.897684</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.665640</td>\n",
       "      <td>1.573439</td>\n",
       "      <td>0.517944</td>\n",
       "      <td>0.925426</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.512747</td>\n",
       "      <td>1.486168</td>\n",
       "      <td>0.573174</td>\n",
       "      <td>0.945279</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.344106</td>\n",
       "      <td>1.222671</td>\n",
       "      <td>0.708832</td>\n",
       "      <td>0.963858</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.272199</td>\n",
       "      <td>1.181986</td>\n",
       "      <td>0.716467</td>\n",
       "      <td>0.972258</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.198307</td>\n",
       "      <td>1.120823</td>\n",
       "      <td>0.742937</td>\n",
       "      <td>0.970476</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.103335</td>\n",
       "      <td>1.085347</td>\n",
       "      <td>0.766098</td>\n",
       "      <td>0.974803</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.097747</td>\n",
       "      <td>1.055618</td>\n",
       "      <td>0.777806</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.057409</td>\n",
       "      <td>1.020130</td>\n",
       "      <td>0.800458</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.998114</td>\n",
       "      <td>0.995424</td>\n",
       "      <td>0.795113</td>\n",
       "      <td>0.982947</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.982624</td>\n",
       "      <td>0.953976</td>\n",
       "      <td>0.825655</td>\n",
       "      <td>0.982184</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.961352</td>\n",
       "      <td>0.983845</td>\n",
       "      <td>0.808603</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.927298</td>\n",
       "      <td>0.947273</td>\n",
       "      <td>0.822601</td>\n",
       "      <td>0.984474</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.895103</td>\n",
       "      <td>0.934988</td>\n",
       "      <td>0.830746</td>\n",
       "      <td>0.982947</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.870866</td>\n",
       "      <td>0.933577</td>\n",
       "      <td>0.826928</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.833527</td>\n",
       "      <td>0.911642</td>\n",
       "      <td>0.842199</td>\n",
       "      <td>0.982438</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.792317</td>\n",
       "      <td>0.869083</td>\n",
       "      <td>0.864088</td>\n",
       "      <td>0.984983</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.714062</td>\n",
       "      <td>0.843529</td>\n",
       "      <td>0.873759</td>\n",
       "      <td>0.987274</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.667765</td>\n",
       "      <td>0.827897</td>\n",
       "      <td>0.876559</td>\n",
       "      <td>0.988547</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.656452</td>\n",
       "      <td>0.821104</td>\n",
       "      <td>0.881395</td>\n",
       "      <td>0.988292</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_metrics()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# e20 results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc = np.array([0.874777, 0.877832, 0.878595, 0.880377, 0.881395])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc.mean(), acc.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# epochs 80"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 80"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /notebooks/data/imagewoof2\n",
      "Learn path /notebooks/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.843249</td>\n",
       "      <td>1.751633</td>\n",
       "      <td>0.459150</td>\n",
       "      <td>0.892848</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.642368</td>\n",
       "      <td>1.551752</td>\n",
       "      <td>0.530415</td>\n",
       "      <td>0.920336</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.472404</td>\n",
       "      <td>1.400948</td>\n",
       "      <td>0.627641</td>\n",
       "      <td>0.947060</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.343889</td>\n",
       "      <td>1.282823</td>\n",
       "      <td>0.682616</td>\n",
       "      <td>0.960804</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.259245</td>\n",
       "      <td>1.150648</td>\n",
       "      <td>0.743701</td>\n",
       "      <td>0.968694</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.159481</td>\n",
       "      <td>1.095263</td>\n",
       "      <td>0.770934</td>\n",
       "      <td>0.971240</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.151687</td>\n",
       "      <td>1.059049</td>\n",
       "      <td>0.780606</td>\n",
       "      <td>0.975821</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.103949</td>\n",
       "      <td>1.072602</td>\n",
       "      <td>0.772461</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.057345</td>\n",
       "      <td>1.041002</td>\n",
       "      <td>0.779333</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.993220</td>\n",
       "      <td>1.026004</td>\n",
       "      <td>0.788241</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.980151</td>\n",
       "      <td>0.950021</td>\n",
       "      <td>0.827182</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.955182</td>\n",
       "      <td>0.961601</td>\n",
       "      <td>0.816238</td>\n",
       "      <td>0.982438</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.914971</td>\n",
       "      <td>0.940982</td>\n",
       "      <td>0.831000</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.894328</td>\n",
       "      <td>0.922552</td>\n",
       "      <td>0.833291</td>\n",
       "      <td>0.982947</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.893008</td>\n",
       "      <td>0.927306</td>\n",
       "      <td>0.830491</td>\n",
       "      <td>0.982693</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.861240</td>\n",
       "      <td>0.941645</td>\n",
       "      <td>0.833036</td>\n",
       "      <td>0.984729</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.832522</td>\n",
       "      <td>0.916425</td>\n",
       "      <td>0.840672</td>\n",
       "      <td>0.981929</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.801349</td>\n",
       "      <td>0.943313</td>\n",
       "      <td>0.828964</td>\n",
       "      <td>0.982693</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.812820</td>\n",
       "      <td>0.902447</td>\n",
       "      <td>0.850344</td>\n",
       "      <td>0.982184</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.794678</td>\n",
       "      <td>0.899070</td>\n",
       "      <td>0.844490</td>\n",
       "      <td>0.983711</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.767048</td>\n",
       "      <td>0.916495</td>\n",
       "      <td>0.835073</td>\n",
       "      <td>0.982438</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>0.762699</td>\n",
       "      <td>0.888912</td>\n",
       "      <td>0.850853</td>\n",
       "      <td>0.984220</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>0.752276</td>\n",
       "      <td>0.891958</td>\n",
       "      <td>0.846526</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>0.734530</td>\n",
       "      <td>0.896443</td>\n",
       "      <td>0.847544</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>0.737241</td>\n",
       "      <td>0.906114</td>\n",
       "      <td>0.846526</td>\n",
       "      <td>0.981929</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>0.729400</td>\n",
       "      <td>0.898970</td>\n",
       "      <td>0.854670</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0.699324</td>\n",
       "      <td>0.896197</td>\n",
       "      <td>0.849580</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.686610</td>\n",
       "      <td>0.891029</td>\n",
       "      <td>0.849835</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.680777</td>\n",
       "      <td>0.885983</td>\n",
       "      <td>0.852380</td>\n",
       "      <td>0.981929</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.695496</td>\n",
       "      <td>0.871883</td>\n",
       "      <td>0.860270</td>\n",
       "      <td>0.982438</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.672372</td>\n",
       "      <td>0.894594</td>\n",
       "      <td>0.851107</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.667111</td>\n",
       "      <td>0.886883</td>\n",
       "      <td>0.855434</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.657499</td>\n",
       "      <td>0.893400</td>\n",
       "      <td>0.849835</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0.652015</td>\n",
       "      <td>0.892052</td>\n",
       "      <td>0.847544</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.641036</td>\n",
       "      <td>0.877710</td>\n",
       "      <td>0.861033</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.644168</td>\n",
       "      <td>0.860512</td>\n",
       "      <td>0.865106</td>\n",
       "      <td>0.982693</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0.634244</td>\n",
       "      <td>0.869056</td>\n",
       "      <td>0.860524</td>\n",
       "      <td>0.982693</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.637747</td>\n",
       "      <td>0.865427</td>\n",
       "      <td>0.857470</td>\n",
       "      <td>0.983456</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.638000</td>\n",
       "      <td>0.867766</td>\n",
       "      <td>0.865869</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.632543</td>\n",
       "      <td>0.861855</td>\n",
       "      <td>0.860524</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.623328</td>\n",
       "      <td>0.848222</td>\n",
       "      <td>0.866124</td>\n",
       "      <td>0.981929</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>0.627719</td>\n",
       "      <td>0.881169</td>\n",
       "      <td>0.851871</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0.628897</td>\n",
       "      <td>0.888570</td>\n",
       "      <td>0.855434</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>0.603168</td>\n",
       "      <td>0.866813</td>\n",
       "      <td>0.858743</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>0.618732</td>\n",
       "      <td>0.882133</td>\n",
       "      <td>0.852889</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.601443</td>\n",
       "      <td>0.863665</td>\n",
       "      <td>0.861288</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.604625</td>\n",
       "      <td>0.863956</td>\n",
       "      <td>0.864851</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>0.606261</td>\n",
       "      <td>0.902055</td>\n",
       "      <td>0.850853</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.600600</td>\n",
       "      <td>0.853932</td>\n",
       "      <td>0.866887</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.608567</td>\n",
       "      <td>0.856600</td>\n",
       "      <td>0.862306</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>0.607223</td>\n",
       "      <td>0.856297</td>\n",
       "      <td>0.865106</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>0.592139</td>\n",
       "      <td>0.876221</td>\n",
       "      <td>0.858743</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>0.598366</td>\n",
       "      <td>0.861327</td>\n",
       "      <td>0.861288</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>0.590205</td>\n",
       "      <td>0.847352</td>\n",
       "      <td>0.864851</td>\n",
       "      <td>0.981929</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>0.596176</td>\n",
       "      <td>0.851996</td>\n",
       "      <td>0.863578</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>0.585699</td>\n",
       "      <td>0.865727</td>\n",
       "      <td>0.865869</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.584858</td>\n",
       "      <td>0.881897</td>\n",
       "      <td>0.858234</td>\n",
       "      <td>0.973276</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.592279</td>\n",
       "      <td>0.878987</td>\n",
       "      <td>0.855179</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>0.587707</td>\n",
       "      <td>0.858519</td>\n",
       "      <td>0.862815</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.584089</td>\n",
       "      <td>0.867480</td>\n",
       "      <td>0.864342</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>0.588298</td>\n",
       "      <td>0.856430</td>\n",
       "      <td>0.865106</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>0.571536</td>\n",
       "      <td>0.876824</td>\n",
       "      <td>0.859252</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.568700</td>\n",
       "      <td>0.838196</td>\n",
       "      <td>0.873250</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.561697</td>\n",
       "      <td>0.840825</td>\n",
       "      <td>0.871469</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.561207</td>\n",
       "      <td>0.837080</td>\n",
       "      <td>0.870450</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.554183</td>\n",
       "      <td>0.833833</td>\n",
       "      <td>0.870960</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.547978</td>\n",
       "      <td>0.832880</td>\n",
       "      <td>0.872741</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.539911</td>\n",
       "      <td>0.827115</td>\n",
       "      <td>0.877832</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.536681</td>\n",
       "      <td>0.816579</td>\n",
       "      <td>0.876050</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.536007</td>\n",
       "      <td>0.828381</td>\n",
       "      <td>0.876050</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.531474</td>\n",
       "      <td>0.815987</td>\n",
       "      <td>0.875032</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.527464</td>\n",
       "      <td>0.826631</td>\n",
       "      <td>0.876559</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.529640</td>\n",
       "      <td>0.802856</td>\n",
       "      <td>0.882158</td>\n",
       "      <td>0.983711</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.524676</td>\n",
       "      <td>0.798292</td>\n",
       "      <td>0.886485</td>\n",
       "      <td>0.982184</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.520193</td>\n",
       "      <td>0.795678</td>\n",
       "      <td>0.886231</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.516949</td>\n",
       "      <td>0.789594</td>\n",
       "      <td>0.888267</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.518372</td>\n",
       "      <td>0.787277</td>\n",
       "      <td>0.887758</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.520692</td>\n",
       "      <td>0.782236</td>\n",
       "      <td>0.894375</td>\n",
       "      <td>0.982184</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.519878</td>\n",
       "      <td>0.783815</td>\n",
       "      <td>0.894121</td>\n",
       "      <td>0.982438</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.519191</td>\n",
       "      <td>0.784481</td>\n",
       "      <td>0.893357</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_metrics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /notebooks/data/imagewoof2\n",
      "Learn path /notebooks/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.906847</td>\n",
       "      <td>1.774188</td>\n",
       "      <td>0.426826</td>\n",
       "      <td>0.881395</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.687434</td>\n",
       "      <td>1.571999</td>\n",
       "      <td>0.528379</td>\n",
       "      <td>0.918809</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.507506</td>\n",
       "      <td>1.423348</td>\n",
       "      <td>0.609570</td>\n",
       "      <td>0.945788</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.361088</td>\n",
       "      <td>1.296032</td>\n",
       "      <td>0.664546</td>\n",
       "      <td>0.959532</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.248759</td>\n",
       "      <td>1.159949</td>\n",
       "      <td>0.733520</td>\n",
       "      <td>0.966404</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.187731</td>\n",
       "      <td>1.106255</td>\n",
       "      <td>0.753881</td>\n",
       "      <td>0.970985</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.129111</td>\n",
       "      <td>1.087642</td>\n",
       "      <td>0.764062</td>\n",
       "      <td>0.971494</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.087413</td>\n",
       "      <td>1.047732</td>\n",
       "      <td>0.788496</td>\n",
       "      <td>0.976584</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.057602</td>\n",
       "      <td>1.024335</td>\n",
       "      <td>0.789768</td>\n",
       "      <td>0.977857</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.000485</td>\n",
       "      <td>0.984618</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.985516</td>\n",
       "      <td>0.991459</td>\n",
       "      <td>0.802749</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.935277</td>\n",
       "      <td>0.992220</td>\n",
       "      <td>0.806821</td>\n",
       "      <td>0.982438</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.910924</td>\n",
       "      <td>0.956274</td>\n",
       "      <td>0.825910</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.910815</td>\n",
       "      <td>0.978738</td>\n",
       "      <td>0.820056</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.871032</td>\n",
       "      <td>0.912943</td>\n",
       "      <td>0.840926</td>\n",
       "      <td>0.982947</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.855911</td>\n",
       "      <td>0.914662</td>\n",
       "      <td>0.838636</td>\n",
       "      <td>0.982184</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.833790</td>\n",
       "      <td>0.935923</td>\n",
       "      <td>0.825910</td>\n",
       "      <td>0.983456</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.803628</td>\n",
       "      <td>0.929826</td>\n",
       "      <td>0.834818</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.817063</td>\n",
       "      <td>0.904890</td>\n",
       "      <td>0.845508</td>\n",
       "      <td>0.982184</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.780370</td>\n",
       "      <td>0.923348</td>\n",
       "      <td>0.834818</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.774644</td>\n",
       "      <td>0.939474</td>\n",
       "      <td>0.818783</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>0.754440</td>\n",
       "      <td>0.906718</td>\n",
       "      <td>0.843217</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>0.734228</td>\n",
       "      <td>0.889890</td>\n",
       "      <td>0.854161</td>\n",
       "      <td>0.983711</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>0.732913</td>\n",
       "      <td>0.896543</td>\n",
       "      <td>0.848562</td>\n",
       "      <td>0.982184</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>0.718545</td>\n",
       "      <td>0.905388</td>\n",
       "      <td>0.845253</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>0.717487</td>\n",
       "      <td>0.877356</td>\n",
       "      <td>0.853652</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0.716731</td>\n",
       "      <td>0.906957</td>\n",
       "      <td>0.849835</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.691277</td>\n",
       "      <td>0.892587</td>\n",
       "      <td>0.852125</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.674897</td>\n",
       "      <td>0.890026</td>\n",
       "      <td>0.852125</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.671073</td>\n",
       "      <td>0.900818</td>\n",
       "      <td>0.851871</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.682480</td>\n",
       "      <td>0.874267</td>\n",
       "      <td>0.861288</td>\n",
       "      <td>0.983965</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.678226</td>\n",
       "      <td>0.896652</td>\n",
       "      <td>0.843726</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.671719</td>\n",
       "      <td>0.878168</td>\n",
       "      <td>0.849835</td>\n",
       "      <td>0.981929</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0.655387</td>\n",
       "      <td>0.872089</td>\n",
       "      <td>0.861288</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.660609</td>\n",
       "      <td>0.868478</td>\n",
       "      <td>0.863833</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.638721</td>\n",
       "      <td>0.890525</td>\n",
       "      <td>0.847289</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0.639555</td>\n",
       "      <td>0.874402</td>\n",
       "      <td>0.857470</td>\n",
       "      <td>0.981420</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.631463</td>\n",
       "      <td>0.891209</td>\n",
       "      <td>0.845762</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.627122</td>\n",
       "      <td>0.880237</td>\n",
       "      <td>0.861797</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.632468</td>\n",
       "      <td>0.867557</td>\n",
       "      <td>0.855688</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.619212</td>\n",
       "      <td>0.862877</td>\n",
       "      <td>0.858488</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>0.619589</td>\n",
       "      <td>0.868979</td>\n",
       "      <td>0.859761</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0.616805</td>\n",
       "      <td>0.863587</td>\n",
       "      <td>0.858743</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>0.614031</td>\n",
       "      <td>0.891636</td>\n",
       "      <td>0.855688</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>0.613589</td>\n",
       "      <td>0.876856</td>\n",
       "      <td>0.860015</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.607392</td>\n",
       "      <td>0.856139</td>\n",
       "      <td>0.868160</td>\n",
       "      <td>0.973785</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.614112</td>\n",
       "      <td>0.877916</td>\n",
       "      <td>0.855179</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>0.597316</td>\n",
       "      <td>0.861688</td>\n",
       "      <td>0.866124</td>\n",
       "      <td>0.982693</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.605737</td>\n",
       "      <td>0.873379</td>\n",
       "      <td>0.856707</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.615907</td>\n",
       "      <td>0.863233</td>\n",
       "      <td>0.861797</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>0.589554</td>\n",
       "      <td>0.854156</td>\n",
       "      <td>0.866378</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>0.600248</td>\n",
       "      <td>0.875490</td>\n",
       "      <td>0.857725</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>0.593159</td>\n",
       "      <td>0.875826</td>\n",
       "      <td>0.859761</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>0.587780</td>\n",
       "      <td>0.867653</td>\n",
       "      <td>0.859252</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>0.594611</td>\n",
       "      <td>0.880475</td>\n",
       "      <td>0.853907</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>0.594087</td>\n",
       "      <td>0.863832</td>\n",
       "      <td>0.867142</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.594692</td>\n",
       "      <td>0.883179</td>\n",
       "      <td>0.858234</td>\n",
       "      <td>0.975821</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.589366</td>\n",
       "      <td>0.882926</td>\n",
       "      <td>0.856707</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>0.586740</td>\n",
       "      <td>0.883274</td>\n",
       "      <td>0.856197</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.589151</td>\n",
       "      <td>0.861009</td>\n",
       "      <td>0.865360</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>0.580238</td>\n",
       "      <td>0.853880</td>\n",
       "      <td>0.867142</td>\n",
       "      <td>0.975821</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>0.569865</td>\n",
       "      <td>0.854630</td>\n",
       "      <td>0.864342</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.570702</td>\n",
       "      <td>0.864635</td>\n",
       "      <td>0.860524</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.560705</td>\n",
       "      <td>0.848361</td>\n",
       "      <td>0.870196</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.556041</td>\n",
       "      <td>0.853485</td>\n",
       "      <td>0.864597</td>\n",
       "      <td>0.975821</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.554787</td>\n",
       "      <td>0.844636</td>\n",
       "      <td>0.867905</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.544290</td>\n",
       "      <td>0.846504</td>\n",
       "      <td>0.866887</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.544165</td>\n",
       "      <td>0.857040</td>\n",
       "      <td>0.864851</td>\n",
       "      <td>0.975821</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.536645</td>\n",
       "      <td>0.829689</td>\n",
       "      <td>0.876304</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.538080</td>\n",
       "      <td>0.824978</td>\n",
       "      <td>0.879613</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.527076</td>\n",
       "      <td>0.821781</td>\n",
       "      <td>0.875795</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.527081</td>\n",
       "      <td>0.813097</td>\n",
       "      <td>0.880886</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.524412</td>\n",
       "      <td>0.810458</td>\n",
       "      <td>0.880631</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.522924</td>\n",
       "      <td>0.806241</td>\n",
       "      <td>0.884449</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.519956</td>\n",
       "      <td>0.808761</td>\n",
       "      <td>0.880886</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.519854</td>\n",
       "      <td>0.802045</td>\n",
       "      <td>0.883685</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.520231</td>\n",
       "      <td>0.798731</td>\n",
       "      <td>0.885722</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.513653</td>\n",
       "      <td>0.799457</td>\n",
       "      <td>0.885722</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.512771</td>\n",
       "      <td>0.799016</td>\n",
       "      <td>0.886231</td>\n",
       "      <td>0.976330</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.513125</td>\n",
       "      <td>0.797723</td>\n",
       "      <td>0.885467</td>\n",
       "      <td>0.977857</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_metrics()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# e80 results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc = np.array([0.893357, 0.885467])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.889412, 0.003944999999999976)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc.mean(), acc.std()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## epochs 80, mixup 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mixup=0.02"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /notebooks/data/imagewoof2\n",
      "Learn path /notebooks/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.892982</td>\n",
       "      <td>1.760529</td>\n",
       "      <td>0.438789</td>\n",
       "      <td>0.888012</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.696311</td>\n",
       "      <td>1.541137</td>\n",
       "      <td>0.543650</td>\n",
       "      <td>0.926953</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.536865</td>\n",
       "      <td>1.524288</td>\n",
       "      <td>0.577755</td>\n",
       "      <td>0.931789</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.415962</td>\n",
       "      <td>1.246328</td>\n",
       "      <td>0.694070</td>\n",
       "      <td>0.964113</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.302436</td>\n",
       "      <td>1.186402</td>\n",
       "      <td>0.729193</td>\n",
       "      <td>0.966658</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.242331</td>\n",
       "      <td>1.138957</td>\n",
       "      <td>0.739628</td>\n",
       "      <td>0.969967</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.172702</td>\n",
       "      <td>1.090300</td>\n",
       "      <td>0.758463</td>\n",
       "      <td>0.972003</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.126768</td>\n",
       "      <td>1.040274</td>\n",
       "      <td>0.786460</td>\n",
       "      <td>0.974803</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.099874</td>\n",
       "      <td>1.048717</td>\n",
       "      <td>0.782387</td>\n",
       "      <td>0.971494</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.031331</td>\n",
       "      <td>0.999306</td>\n",
       "      <td>0.798676</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.022667</td>\n",
       "      <td>1.022185</td>\n",
       "      <td>0.793332</td>\n",
       "      <td>0.972767</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.001567</td>\n",
       "      <td>0.966803</td>\n",
       "      <td>0.812166</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.945884</td>\n",
       "      <td>0.982788</td>\n",
       "      <td>0.813184</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.946741</td>\n",
       "      <td>0.996584</td>\n",
       "      <td>0.805039</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.908163</td>\n",
       "      <td>0.914809</td>\n",
       "      <td>0.838890</td>\n",
       "      <td>0.983965</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.886255</td>\n",
       "      <td>0.900401</td>\n",
       "      <td>0.840163</td>\n",
       "      <td>0.982693</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.891019</td>\n",
       "      <td>0.943897</td>\n",
       "      <td>0.838890</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.881711</td>\n",
       "      <td>0.916968</td>\n",
       "      <td>0.833036</td>\n",
       "      <td>0.982693</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.847849</td>\n",
       "      <td>0.916726</td>\n",
       "      <td>0.835836</td>\n",
       "      <td>0.984474</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.829580</td>\n",
       "      <td>0.889951</td>\n",
       "      <td>0.849580</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.819297</td>\n",
       "      <td>0.899683</td>\n",
       "      <td>0.847544</td>\n",
       "      <td>0.981420</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>0.803582</td>\n",
       "      <td>0.882974</td>\n",
       "      <td>0.857725</td>\n",
       "      <td>0.982947</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>0.789698</td>\n",
       "      <td>0.886073</td>\n",
       "      <td>0.855688</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>0.777672</td>\n",
       "      <td>0.895599</td>\n",
       "      <td>0.849326</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>0.762550</td>\n",
       "      <td>0.885553</td>\n",
       "      <td>0.851107</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>0.756742</td>\n",
       "      <td>0.877604</td>\n",
       "      <td>0.854416</td>\n",
       "      <td>0.983202</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0.735611</td>\n",
       "      <td>0.909271</td>\n",
       "      <td>0.844744</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.736039</td>\n",
       "      <td>0.896097</td>\n",
       "      <td>0.853143</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.751848</td>\n",
       "      <td>0.879653</td>\n",
       "      <td>0.851616</td>\n",
       "      <td>0.982947</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.735013</td>\n",
       "      <td>0.881263</td>\n",
       "      <td>0.855434</td>\n",
       "      <td>0.981929</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.707418</td>\n",
       "      <td>0.892651</td>\n",
       "      <td>0.851362</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.714809</td>\n",
       "      <td>0.885933</td>\n",
       "      <td>0.852125</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.704837</td>\n",
       "      <td>0.882306</td>\n",
       "      <td>0.853652</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0.702029</td>\n",
       "      <td>0.868731</td>\n",
       "      <td>0.859506</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>01:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.691113</td>\n",
       "      <td>0.886638</td>\n",
       "      <td>0.847798</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.686075</td>\n",
       "      <td>0.881708</td>\n",
       "      <td>0.852380</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0.698079</td>\n",
       "      <td>0.886434</td>\n",
       "      <td>0.857470</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.679332</td>\n",
       "      <td>0.891835</td>\n",
       "      <td>0.851871</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.675134</td>\n",
       "      <td>0.858760</td>\n",
       "      <td>0.864597</td>\n",
       "      <td>0.981420</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.672610</td>\n",
       "      <td>0.860570</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.670927</td>\n",
       "      <td>0.888854</td>\n",
       "      <td>0.850853</td>\n",
       "      <td>0.976584</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>0.658757</td>\n",
       "      <td>0.857907</td>\n",
       "      <td>0.864597</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0.658850</td>\n",
       "      <td>0.859863</td>\n",
       "      <td>0.860524</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>0.659535</td>\n",
       "      <td>0.861954</td>\n",
       "      <td>0.865615</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>0.663616</td>\n",
       "      <td>0.868347</td>\n",
       "      <td>0.862051</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>01:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.660933</td>\n",
       "      <td>0.873969</td>\n",
       "      <td>0.854416</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.648863</td>\n",
       "      <td>0.867569</td>\n",
       "      <td>0.861288</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>0.645474</td>\n",
       "      <td>0.875552</td>\n",
       "      <td>0.856452</td>\n",
       "      <td>0.976584</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.639098</td>\n",
       "      <td>0.879108</td>\n",
       "      <td>0.855688</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.645620</td>\n",
       "      <td>0.851229</td>\n",
       "      <td>0.860524</td>\n",
       "      <td>0.982184</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>0.643645</td>\n",
       "      <td>0.853845</td>\n",
       "      <td>0.867142</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>0.638872</td>\n",
       "      <td>0.858342</td>\n",
       "      <td>0.866378</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>0.648289</td>\n",
       "      <td>0.849843</td>\n",
       "      <td>0.855179</td>\n",
       "      <td>0.984220</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>0.630311</td>\n",
       "      <td>0.856275</td>\n",
       "      <td>0.868669</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>0.631352</td>\n",
       "      <td>0.870802</td>\n",
       "      <td>0.859506</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>0.630236</td>\n",
       "      <td>0.851790</td>\n",
       "      <td>0.864597</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.638142</td>\n",
       "      <td>0.863742</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.639889</td>\n",
       "      <td>0.838462</td>\n",
       "      <td>0.873759</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>0.628044</td>\n",
       "      <td>0.837487</td>\n",
       "      <td>0.872741</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.631659</td>\n",
       "      <td>0.839269</td>\n",
       "      <td>0.874523</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>0.622913</td>\n",
       "      <td>0.841304</td>\n",
       "      <td>0.870960</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>0.621546</td>\n",
       "      <td>0.841167</td>\n",
       "      <td>0.870705</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.620193</td>\n",
       "      <td>0.858885</td>\n",
       "      <td>0.865869</td>\n",
       "      <td>0.974803</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.616341</td>\n",
       "      <td>0.848921</td>\n",
       "      <td>0.865106</td>\n",
       "      <td>0.975821</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.606583</td>\n",
       "      <td>0.841471</td>\n",
       "      <td>0.870450</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.598344</td>\n",
       "      <td>0.825172</td>\n",
       "      <td>0.876050</td>\n",
       "      <td>0.983711</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.601000</td>\n",
       "      <td>0.845760</td>\n",
       "      <td>0.866378</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.580514</td>\n",
       "      <td>0.829793</td>\n",
       "      <td>0.873505</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.585908</td>\n",
       "      <td>0.821322</td>\n",
       "      <td>0.877068</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.590058</td>\n",
       "      <td>0.826795</td>\n",
       "      <td>0.876559</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.573327</td>\n",
       "      <td>0.813736</td>\n",
       "      <td>0.883940</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.571503</td>\n",
       "      <td>0.795879</td>\n",
       "      <td>0.891066</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.574700</td>\n",
       "      <td>0.807385</td>\n",
       "      <td>0.883431</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.570341</td>\n",
       "      <td>0.796195</td>\n",
       "      <td>0.883176</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.559908</td>\n",
       "      <td>0.790318</td>\n",
       "      <td>0.889794</td>\n",
       "      <td>0.981420</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.558701</td>\n",
       "      <td>0.788275</td>\n",
       "      <td>0.893103</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.556780</td>\n",
       "      <td>0.789497</td>\n",
       "      <td>0.892339</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.556308</td>\n",
       "      <td>0.784988</td>\n",
       "      <td>0.893612</td>\n",
       "      <td>0.981929</td>\n",
       "      <td>01:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.561333</td>\n",
       "      <td>0.785652</td>\n",
       "      <td>0.892594</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.561363</td>\n",
       "      <td>0.784571</td>\n",
       "      <td>0.892339</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>01:43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs,mixup=mixup)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.recorder.plot_metrics()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.recorder.plot_metrics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
